Reinforcement Learning Series: Overview of Methods Steve Brunton 2026-09-30 | Reinforcement Learning Series: Overview of Methods
Residual Networks (ResNet) [Physics Informed Machine Learning] Steve Brunton 2024-06-16 | This video discusses Residual Networks, one of the most popular machine learning architectures that has enabled considerably deeper neural networks through jump/skip connections. This architecture mimics many of the aspects of a numerical integrator. This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company%%% CHAPTERS %%%00:00 Intro01:09 Concept: Modeling the Residual03:26 Building Blocks05:59 Motivation: Deep Network Signal Loss07:43 Extending to Classification09:00 Extending to DiffEqs10:16 Impact of CVPR and Resnet12:17 Resnets and Euler Integrators13:34 Neural ODEs and Improved Integrators16:07 Outro
Neural ODEs (NODEs) [Physics Informed Machine Learning] Steve Brunton 2024-06-06 | This video describes Neural ODEs, a powerful machine learning approach to learn ODEs from data. This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company%%% CHAPTERS %%%00:00 Intro02:09 Background: ResNet05:05 From ResNet to ODE07:59 ODE Essential Insight/ Why ODE outperforms ResNet// 09:05 ODE Essential Insight Rephrase 1// 09:54 ODE Essential Insight Rephrase 211:11 ODE Performance vs ResNet Performance12:52 ODE extension: HNNs14:03 ODE extension: LNNs14:45 ODE algorithm overview/ ODEs and Adjoint Calculation22:24 Outro
Physics Informed Neural Networks (PINNs) [Physics Informed Machine Learning] Steve Brunton 2024-05-29 | This video introduces PINNs, or Physics Informed Neural Networks. PINNs are a simple modification of a neural network that adds a PDE in the loss function to promote solutions that satisfy known physics. For example, if we wish to model a fluid flow field and we know it is incompressible, we can add the divergence of the field in the loss function to drive it towards zero. This approach relies on the automatic differentiability in neural networks (i.e., backpropagation) to compute partial derivatives used in the PDE loss function. Original PINNs paper: sciencedirect.com/science/article/abs/pii/S0021999118307125Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equationsM. Raissi P. Perdikaris, G.E. KarniadakisJournal of Computational PhysicsVolume 378: 686-707, 2019This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company%%% CHAPTERS %%%00:00 Intro01:54 PINNs: Central Concept06:38 Advantages and Disadvantages11:39 PINNs and Inference15:23 Recommended Resources19:33 Extending PINNs: Fractional PINNs21:40 Extending PINNs: Delta PINNs25:33 Failure Modes29:40 PINNs & Pareto Fronts31:57 Outro
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning Steve Brunton 2024-05-16 | SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learningby Nicholas Zolman, Urban Fasel, J. Nathan Kutz, Steven L. Bruntonarxiv paper: arxiv.org/abs/2403.09110github code: github.com/nzolman/sindy-rlDeep reinforcement learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in environments with complicated dynamics, such as stabilizing the magnetohydrodynamics of a tokamak fusion reactor or minimizing the drag force exerted on an object in a fluid flow. However, these algorithms require an abundance of training examples and may become prohibitively expensive for many applications. In addition, the reliance on deep neural networks often results in an uninterpretable, black-box policy that may be too computationally expensive to use with certain embedded systems. Recent advances in sparse dictionary learning, such as the sparse identification of nonlinear dynamics (SINDy), have shown promise for creating efficient and interpretable data-driven models in the low-data regime. In this work we introduce SINDy-RL, a unifying framework for combining SINDy and DRL to create efficient, interpretable, and trustworthy representations of the dynamics model, reward function, and control policy. We demonstrate the effectiveness of our approaches on benchmark control environments and challenging fluids problems. SINDy-RL achieves comparable performance to state-of-the-art DRL algorithms using significantly fewer interactions in the environment and results in an interpretable control policy orders of magnitude smaller than a deep neural network policy.%%% CHAPTERS %%%00:00 Intro01:25 What is Reinforcement Learning?03:12 Reinforcement Learning Drawbacks05:20 Dictionary Learning and SINDy06:55 SINDy-RL: Environment11:42 SINDy-RL: Reward23:25 SINDy-RL: Agent14:48 SINDy-RL: Uncertainty Quantification20:07 Recap and Outro
AI/ML+Physics: Preview of Upcoming Modules and Bootcamps [Physics Informed Machine Learning] Steve Brunton 2024-05-03 | This video provides a brief preview of the upcoming modules and bootcamps in this series on Physics Informed Machine Learning. Topics include: (1) Parsimonious modeling and SINDy; (2) Physics informed neural networks (PINNs); (3) Operator methods, like DeepONets and Fourier Neural Operators; (4) Symmetries in physics and machine learning; (5) Digital Twin technology; and (6) Case studies in engineering.This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company%%% CHAPTERS %%%00:00 Intro & Recap01:06 Reviewing the 5 Stages04:08 Reviewing Physics in the Stages05:11 Why Physical Models: Cost & Data Scale07:53 Why Physical Models: Generalized Models10:01 Why Physcial Models: Discovering Physics11:40 Holistic Impact of Embedding Physics // Struggling to find a good wording here12:55 Case Study: Pendulum Data and SINDy15:20 Case Study: Symbolic Regression and Evolutionary Optimization16:45 Case Study: Lagrangian Neural Networks18:34 Architectures and Symmetries19:36 Applications in Engineering21:21 The Digital Twin22:15 Benchmark Problems23:35 Outro
AI/ML+Physics: Recap and Summary [Physics Informed Machine Learning] Steve Brunton 2024-04-26 | This video provides a brief recap of this introductory series on Physics Informed Machine Learning. We revisit the five stages of machine learning, and how physics may be incorporated into these stages. We also discuss architectures, symmetries, the digital twin, applications in engineering, and the importance of dynamical systems and controls benchmarks.This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company%%% CHAPTERS %%%00:00 Intro00:24 Future Modules06:06 Curriculum Framework07:02 The Dual Problems of PIML08:45 Data-Driven Science and Engineering09:12 Sneak Peak of the Modules09:35 Sneak Peak: Parsimonious Models11:13 Sneak Peak: PINNs12:47 Sneak Peak: Operator Methods14:10 Sneak Peak: Symmetries15:42 Sneak Peak: Digital Twins17:35 Sneak Peak: Case Studies & Benchmarks18:24 Outro
Using sparse trajectory data to find Lagrangian Coherent Structures (LCS) in fluid flows Steve Brunton 2024-04-12 | Video by Tanner Harms, based on "Lagrangian Gradient Regression for the Detection of Coherent Structures from Sparse Trajectory Data"by Tanner D. Harms, Steven L. Brunton, Beverley J. McKeonarxiv.org/abs/2310.10994The method of Lagrangian Coherent Structures (LCS) uses particle trajectories in fluid flows to identify coherent structures that govern the behavior of the flow. The typical methods employed to identify LCS rely on a dense grid of numerical tracers which are seeded onto the pre-computed vector fields and advected through time and space. However, in many systems, dense flow field information is not available, and researchers must perform their analyses on a sparse set of tracers that already exist in the flow. For example, if we wish our flow observer to autonomously make decisions based on the flow field information, then the computational expense of the normal LCS identification pipeline is too costly to use. It will need to use only the trajectories of the tracers that it sees in the flow. Motivated by the desire to study flow fields autonomously, this video shows how sparse trajectory data can be leveraged to identify key flow quantities including the velocity gradient, Finite-Time Lyapunov Exponent (FTLE), and Lagrangian-Averaged Vorticity Deviation (LAVD), which are often used to identify LCS. Key links:youtube.com/watch?v=lveOu7jLNh0arxiv.org/abs/2310.10994pubs.aip.org/aip/cha/article-abstract/20/1/017503/280647/Fast-computation-of-finite-time-Lyapunov-exponent?redirectedFrom=fulltextamazon.com/Transport-Barriers-Coherent-Structures-Flow/dp/1009225170/ref=sr_1_1?crid=2B4GREV4UGIC8&dib=eyJ2IjoiMSJ9.qjMjRA8SBCd-3Ae8SG6jGbX6bldhU7BK3jvLRjmuEKxcPr_qK4lUpwnrmT3A280AdIFVFal92uVoxHmYI1jpY2XQlpsIV-rD_mGA8h22VZ07bINHllI1OO8VkK2N1FAoJ6y-Z-vR9U4FVUE_mu4GLaWBVmqofJuNABo_uyrK_jDrWoQa4Qcr3opSl-6t7EadHjHDXccJ-LbLpO97oB-pfz3HWVjbBj2znEH-2cZKYYw.msqHpb8gaubQ_rfWE_8DaBpRAAPjVHOJX5J4KMt4Ia8&dib_tag=se&keywords=george+haller&qid=1708992219&sprefix=george+hall%2Caps%2C359&sr=8-1pubs.aip.org/aip/cha/article/25/9/097617/134953
AI/ML+Physics Part 5: Employing an Optimization Algorithm [Physics Informed Machine Learning] Steve Brunton 2024-04-05 | This video discusses the fifth stage of the machine learning process: (5) selecting and implementing an optimization algorithm to train the model. There are opportunities to incorporate physics into this stage of the process, such as using constrained optimization to force a model onto a susbpace or submanifold characterized by a symmetry or other physical constraint.This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company%%% CHAPTERS %%%00:00 Intro01:45 Case Study: KKT Constrained Least Squares06:18 Case Study: Physics Informed DMD14:00 Loss vs Optimization of Subspace Constraints17:50 Subspace Constraints and Symmetry19:28 Case Study: Symbolic Regression and Evolutionary Optimization22:25 Parsimony and Sparse Optimization Algorithms25:03 Case Study: SINDy and SR328:38 Parsimony and Sparsity Hyperparameters30:55 Outro
The Future of Model Based Engineering: Collimator 2.0 Steve Brunton 2024-03-31 | Learn more at collimator.aiCollimator allows you to model, simulate, optimize, control, and collaborate in the cloud, with the power of Python and JAXNew features: * Powered by JAX * Generative AI * Auto-Differentiation * PID Auto-Tune * SINDy model blocks * Model Predictive Control * Real-Time Collaboration * Hardware in the Loop * Hybrid models * State machines * FMU support * Updated Python block Steve Brunton is the Chief Scientist of Collimator%%% CHAPTERS %%%0:25 Powered by JAX1:51 Generative AI3:49 Hardware in the Loop5:46 Real-time Collaboration6:28 Automatic Differentiation8:52 PID Autotune10:28 Model Predictive Control11:06 Recap
AI/ML+Physics Part 4: Crafting a Loss Function [Physics Informed Machine Learning] Steve Brunton 2024-03-22 | This video discusses the fourth stage of the machine learning process: (4) designing a loss function to assess the performance of the model. There are opportunities to incorporate physics into this stage of the process, such as adding regularization terms to promote sparsity or extra loss functions to ensure that a partial differential equation is satisfied, as in PINNs.This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company%%% CHAPTERS %%%00:00 Intro00:55 Case Study: Fluid Velocity & Navier-Stokes05:56 Case Study: Incompressible Flows & Poisson07:46 Case Study: Lagrangian Neural Networks & Euler-Lagrange09:38 Sparse Loss and the L1 Norm12:51 Case Study: SINDy + AutoEncoder15:41 SINDy and Loss Regularization17:59 Parsimonious Modeling20:16 Equivariant Loss21:59 Outro
AI/ML+Physics Part 3: Designing an Architecture [Physics Informed Machine Learning] Steve Brunton 2024-03-08 | This video discusses the third stage of the machine learning process: (3) choosing an architecture with which to represent the model. This is one of the most exciting stages, including all of the new architectures, such as UNets, ResNets, SINDy, PINNs, Operator networks, and many more. There are opportunities to incorporate physics into this stage of the process, such as incorporating known symmetries through custom equivariant layers.This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company%%% CHAPTERS %%%00:00 Intro01:03 The Architecture Zoo/Architectures Overview06:29 What is Physics?12:38 Case Study: Pendulum17:10 Defining a Function Space20:51 Case Studies: Physics Informed Architectures23:36 ResNets24:26 UNets25:15 Physics Informed Neural Networks26:50 Lagrangian Neural Networks27:24 Deep Operator Networks27:49 Fourier Neural Operators28:23 Graph Neural Networks30:02 Invariance and Equivariance35:59 Outro
AI/ML+Physics Part 2: Curating Training Data [Physics Informed Machine Learning] Steve Brunton 2024-03-01 | This video discusses the second stage of the machine learning process: (2) collecting and curating training data to inform the model. There are opportunities to incorporate physics into this stage of the process, such as data augmentation to incorporate known symmetries. This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company%%% CHAPTERS %%%00:00 Intro03:02 Augmenting Data with Physics04:16 Coordinates Matter!06:38 Simulated vs Experimental Data10:42 Big Data vs Diverse Data12:48 Generalizing Models with Physics16:31 Data is Expensive17:42 Data is Biased18:58 Rare Events21:24 Small Signals24:13 Galileo Dropped the Ball27:10 Hidden Variables29:22 Preview: Discovering Governing Equations30:42 The Digital Twin35:09 Outro
AI/ML+Physics Part 1: Choosing what to model [Physics Informed Machine Learning] Steve Brunton 2024-02-23 | This video discusses the first stage of the machine learning process: (1) formulating a problem to model. There are lots of opportunities to incorporate physics into this process, and learn new physics by applying ML to the right problem. This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company%%% CHAPTERS %%%00:00 Intro04:51 Deciding on the Problem07:08 Why do you need an ML Model?14:54 Case Study: Super Resolution17:07 Case Study: Discovering New Physics18:37 Case Study: Materials Discovery19:12 Case Study: Computational Chemistry20:50 Case Study: Digital Twins & Discrepancy Models21:56 Case Study: Shape Optimization25:13 The Digital Twin29:16 Modeling the Math33:31 Modeling the Chaos34:18 Case Study: Climate Modeling35:08 Benchmark Systems35:47 Case Study: Turbulence Closure Modeling39:16 When not to use Machine Learning42:15 Outro
Physics Informed Machine Learning: High Level Overview of AI and ML in Science and Engineering Steve Brunton 2024-02-16 | This video describes how to incorporate physics into the machine learning process. The process of machine learning is broken down into five stages: (1) formulating a problem to model, (2) collecting and curating training data to inform the model, (3) choosing an architecture with which to represent the model, (4) designing a loss function to assess the performance of the model, and (5) selecting and implementing an optimization algorithm to train the model. At each stage, we discuss how prior physical knowledge may be embedding into the process. Physics informed machine learning is critical for many engineering applications, since many engineering systems are governed by physics and involve safety critical components. It also makes it possible to learn more from sparse and noisy data sets. This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company%%% CHAPTERS %%%00:00 Intro03:53 What is Physics Informed Machine Learning?06:41 Case Study: Encoding Pendulum Movement09:19 The Five Stages of Machine Learning16:09 A Principled Approach to Machine Learning20:00 Physics Informed Problem Modeling21:48 Physics Informed Data Curation25:34 Physics Informed Architecture Design28:59 Physics Informed Loss Functions30:55 Physics Informed Optimization Algorithms34:56 What This Course Will Cover46:48 Outro
Can we make commercial aircraft faster? Mitigating transonic buffet with porous trailing edges Steve Brunton 2024-01-25 | Extending the aircraft flight envelope by mitigating transonic airfoil buffet by Esther Lagemann, Steven L. Brunton, Wolfgang Schröder, Christian Lagemann arXiv: arxiv.org/abs/2401.08894 In the age of globalization, commercial aviation plays a central role in maintaining our international connectivity by providing fast air transport services for passengers and freight. However, the upper limit of the aircraft flight envelope, essentially the maximum aircraft speed, is usually fixed by the occurrence of a safety-critical aerodynamic phenomenon called transonic airfoil buffet. It refers to shock wave oscillations occurring on the aircraft wings, which induce unsteady aerodynamic loads acting on the wing structure. Since these loads can cause severe structural damage endangering flight safety, the aviation industry is highly interested in suppressing transonic airfoil buffet to extend the flight envelope to higher aircraft speeds. In this contribution, we demonstrate with experimental wind tunnel measurements that the application of porous trailing edges substantially attenuates the buffet phenomenon. Since porous trailing edges have the additional benefit of reducing acoustic aircraft emissions, our findings could pave the way for faster air transport with reduced noise emissions.
A Neural Network Primer Steve Brunton 2024-01-05 | [Tier 1, Lecture 04c] This video provides a primer on neural networks for machine learning and artificial intelligence. Neural networks are biologically inspired and provide the backbone of many modern ML/AI frameworks.This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company%%% CHAPTERS %%%0:00 Overview2:15 What is a Neural Network?5:17 The Perceptron (History of Neural Networks)6:39 Deep Learning8:50 A Diversity of Architectures: the Neural Network Zoo11:30 CNN: Convolutional Neural Networks13:11 RNN: Recurrent Neural Networks14:01 Autoencoder Networks16:20 Outro
Supervised & Unsupervised Machine Learning Steve Brunton 2023-12-30 | [Tier 1, Lecture 4b] This video describes the two main categories of machine learning: supervised and unsupervised learning. Supervised learning involves labeled training data, where the ground truth is included in the training data, while unsupervised learning does not include these labels.This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company%%% CHAPTERS %%%0:00 Overview1:45 Detailed Categorization of Machine Learning2:19 Supervised vs Unsupervised Learning8:07 Reinforcement Learning
A Machine Learning Primer: How to Build an ML Model Steve Brunton 2023-12-29 | [Tier 1, Lecture 4a] This video provides a primer on the types of machine learning (ML) and their uses, including: 1) what is a model; 2) what are types of ML; what are the major stages involved in training an ML model.This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company%%% CHAPTERS %%%0:00 Overview0:49 Machine Learning is Not Magic 1:29 Machine Learning is Optimization3:23 What is a Machine Learning Model?6:24 Categorizing Types of Machine Learning8:36 Stages of Training a Machine Learning Model12:49 How to Incorporate Physics into Machine Learning
Arousal as a universal embedding for spatiotemporal brain dynamics Steve Brunton 2023-12-22 | Video abstract for “Arousal as a universal embedding for spatiotemporal brain dynamics” by Ryan V. Raut, Zachary P. Rosenthal, Xiaodan Wang, Hanyang Miao, Zhanqi Zhang, Jin-Moo Lee, Marcus E. Raichle, Adam Q. Bauer, Steven L. Brunton, Bingni W. Brunton, and J. Nathan Kutz biorxiv.org/content/10.1101/2023.11.06.565918v2Neural activity in awake organisms shows widespread and spatiotemporally diverse correlations with behavioral and physiological measurements. We propose that this covariation reflects in part the dynamics of a unified, arousal-related process that regulates brain-wide physiology on the timescale of seconds. Taken together with theoretical foundations in dynamical systems, this interpretation leads us to a surprising prediction: that a single, scalar measurement of arousal (e.g., pupil diameter) should suffice to reconstruct the continuous evolution of multimodal, spatiotemporal measurements of large-scale brain physiology. To test this hypothesis, we perform multimodal, cortex-wide optical imaging and behavioral monitoring in awake mice. We demonstrate that spatiotemporal measurements of neuronal calcium, metabolism, and blood-oxygen can be accurately and parsimoniously modeled from a low-dimensional state-space reconstructed from the time history of pupil diameter. Extending this framework to behavioral and electrophysiological measurements from the Allen Brain Observatory, we demonstrate the ability to integrate diverse experimental data into a unified generative model via mappings from an intrinsic arousal manifold. Our results support the hypothesis that spontaneous, spatially structured fluctuations in brain-wide physiology—widely interpreted to reflect regionally-specific neural communication—are in large part reflections of an arousal-related process. This enriched view of arousal dynamics has broad implications for interpreting observations of brain, body, and behavior as measured across modalities, contexts, and scales.
New Advances in Artificial Intelligence and Machine Learning Steve Brunton 2023-12-15 | [Tier 1, Lecture 3] This video describes modern advances in machine learning and artificial intelligence, which are rapidly evolving technologies. Topics include generative AI (diffusion models, DALL-E 2, ChatGPT, etc.), reinforcement learning, computer vision, etc.This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company%%% CHAPTERS %%%0:00 Overview1:04 Image Classification 3:14 The Importance of Training Data7:19 Generative Images8:22 Image Captioning9:18 DALL-E 211:08 History of Deep Dream14:15 Text Generation and NLP16:24 ChatGPT and LLMs18:55 What is ML good at?20:36 Reinforcement Learning and Atari24:45 Chaos and Weather26:45 Outro
Nonlinear parametric models of viscoelastic fluid flows with SINDy Steve Brunton 2023-12-01 | Nonlinear parametric models of viscoelastic fluid flowsby Cassio M. Oishi, Alan A. Kaptanoglu, J. Nathan Kutz, Steven L. Bruntonarxiv.org/abs/2308.04405Reduced-order models have been widely adopted in fluid mechanics, particularly in the context of Newtonian fluid flows. These models offer the ability to predict complex dynamics, such as instabilities and oscillations, at a considerably reduced computational cost. In contrast, the reduced-order modeling of non-Newtonian viscoelastic fluid flows remains relatively unexplored. This work leverages the sparse identification of nonlinear dynamics (SINDy) algorithm to develop interpretable reduced-order models for a broad class of viscoelastic flows. In particular, we explore a benchmark oscillatory viscoelastic flow on the four-roll mill geometry using the classical Oldroyd-B fluid. This flow exemplifies many canonical challenges associated with non-Newtonian flows, including transitions, asymmetries, instabilities, and bifurcations arising from the interplay of viscous and elastic forces, all of which require expensive computations in order to resolve the fast timescales and long transients characteristic of such flows. First, we demonstrate the effectiveness of our data-driven surrogate model in predicting the transient evolution on a simplified representation of the dynamical system. We then describe the ability of the reduced-order model to accurately reconstruct spatial flow field in a basis obtained via proper orthogonal decomposition. Finally, we develop a fully parametric, nonlinear model that captures the dominant variations of the dynamics with the relevant nondimensional Weissenberg number. This work illustrates the potential to reduce computational costs and improve design, optimization, and control of a large class of non-Newtonian fluid flows with modern machine learning and reduced-order modeling techniques.
[5/8] Control for Societal-Scale Challenges: Road Map 2030 [Technology, Validation, and Transition] Steve Brunton 2023-06-30 | This video explores the need for validation and benchmark problems to transition technology to broad industrial applications. This follows Chapter 5 of the "Control for Societal-Scale Challenges: Road Map 2030" document. ieeecss.org/control-societal-scale-challenges-road-map-2030The production of this video was supported by the IFAC. Roadmap Abstract: The world faces some of its greatest challenges of modern time and how we address them will have a dramatic impact on the life for generations to come. Simultaneously, control systems, consisting of information enriched by various degrees of analytics followed by decision-making, are pervading a variety of sectors, not only in engineering but beyond, into financial services, socio-economic analysis, entertainment and sports, and political and social sciences. Increased levels of automation are sought after in various sectors and being introduced into new domains. All of these advances and transformations urge a shift in the conversation toward how control systems can meet grand societal- scale challenges. The document seeks to chart a roadmap for the evolution of control systems, identifying several areas where our discipline can have an impact over the next decade.Edited by: Anuradha M. Annaswamy, Karl H. Johansson, and George J. PappasAuthors: Andrew Alleyne, Frank Allgöwer, Aaron D. Ames, Saurabh Amin, James Anderson, Anuradha M. Annaswamy, Panos J. Antsaklis, Neda Bagheri, Hamsa Balakrishnan, Bassam Bamieh, John Baras, Margret Bauer, Alexandre Bayen, Paul Bogdan, Steven L. Brunton, Francesco Bullo, Etienne Burdet, Joel Burdick, Laurent Burlion, Carlos Canudas de Wit, Ming Cao, Christos G. Cassandras, Aranya Chakrabortty, Giacomo Como, Marie Csete, Fabrizio Dabbene, Munther Dahleh, Amritam Das, Eyal Dassau, Claudio De Persis, Mario di Bernardo, Stefano Di Cairano, Dimos V. Dimarogonas, Florian Dörfler, John C. Doyle, Francis J. Doyle III, Anca Dragan, Magnus Egerstedt, Johan Eker, Sarah Fay, Dimitar Filev, Angela Fontan, Elisa Franco, Masayuki Fujita, Mario Garcia-Sanz, Dennice Gayme, Wilhelmus P.M.H. Heemels, João P. Hespanha, Sandra Hirche, Anette Hosoi, Jonathan P. How, Gabriela Hug, Marija Ilić, Hideaki Ishii, Ali Jadbabaie, Matin Jafarian, Samuel Qing-Shan Jia, Tor Arne Johansen, Karl H. Johansson, Dalton Jones, Mustafa Khammash, Pramod Khargonekar, Mykel J. Kochenderfer, Andreas Krause, Anthony Kuh, Dana Kulić, Françoise Lamnabhi-Lagarrigue, Naomi E. Leonard, Frederick Leve, Na Li, Steven Low, John Lygeros, Iven Mareels, Sonia Martinez, Nikolai Matni, Tommaso Menara, Katja Mombaur, Kevin Moore, Richard Murray, Toru Namerikawa, Angelia Nedich, Sandeep Neema, Mariana Netto, Timothy O’Leary, Marcia K. O’Malley, Lucy Y. Pao, Antonis Papachristodoulou, George J. Pappas, Philip E. Paré, Thomas Parisini, Fabio Pasqualetti, Marco Pavone, Akshay Rajhans, Gireeja Ranade, Anders Rantzer, Lillian Ratliff, J. Anthony Rossiter, Dorsa Sadigh, Tariq Samad, Henrik Sandberg, Sri Sarma, Luca Schenato, Jacquelien Scherpen, Angela Schoellig, Rodolphe Sepulchre, Jeff Shamma, Robert Shorten, Bruno Sinopoli, Koushil Sreenath, Jakob Stoustrup, Jing Sun, Paulo Tabuada, Emma Tegling, Dawn Tilbury, Claire J. Tomlin, Jana Tumova, Kevin Wise, Dan Work, Junaid Zafar, Melanie ZeilingerThis video was produced at the University of Washington
[8/8] Control for Societal-Scale Challenges: Road Map 2030 [Recommendations] Steve Brunton 2023-06-30 | This video concludes the roadmap with recommendations and key take aways. This follows Chapter 8 of the "Control for Societal-Scale Challenges: Road Map 2030" document. ieeecss.org/control-societal-scale-challenges-road-map-2030The production of this video was supported by the IFAC. Roadmap Abstract: The world faces some of its greatest challenges of modern time and how we address them will have a dramatic impact on the life for generations to come. Simultaneously, control systems, consisting of information enriched by various degrees of analytics followed by decision-making, are pervading a variety of sectors, not only in engineering but beyond, into financial services, socio-economic analysis, entertainment and sports, and political and social sciences. Increased levels of automation are sought after in various sectors and being introduced into new domains. All of these advances and transformations urge a shift in the conversation toward how control systems can meet grand societal- scale challenges. The document seeks to chart a roadmap for the evolution of control systems, identifying several areas where our discipline can have an impact over the next decade.Edited by: Anuradha M. Annaswamy, Karl H. Johansson, and George J. PappasAuthors: Andrew Alleyne, Frank Allgöwer, Aaron D. Ames, Saurabh Amin, James Anderson, Anuradha M. Annaswamy, Panos J. Antsaklis, Neda Bagheri, Hamsa Balakrishnan, Bassam Bamieh, John Baras, Margret Bauer, Alexandre Bayen, Paul Bogdan, Steven L. Brunton, Francesco Bullo, Etienne Burdet, Joel Burdick, Laurent Burlion, Carlos Canudas de Wit, Ming Cao, Christos G. Cassandras, Aranya Chakrabortty, Giacomo Como, Marie Csete, Fabrizio Dabbene, Munther Dahleh, Amritam Das, Eyal Dassau, Claudio De Persis, Mario di Bernardo, Stefano Di Cairano, Dimos V. Dimarogonas, Florian Dörfler, John C. Doyle, Francis J. Doyle III, Anca Dragan, Magnus Egerstedt, Johan Eker, Sarah Fay, Dimitar Filev, Angela Fontan, Elisa Franco, Masayuki Fujita, Mario Garcia-Sanz, Dennice Gayme, Wilhelmus P.M.H. Heemels, João P. Hespanha, Sandra Hirche, Anette Hosoi, Jonathan P. How, Gabriela Hug, Marija Ilić, Hideaki Ishii, Ali Jadbabaie, Matin Jafarian, Samuel Qing-Shan Jia, Tor Arne Johansen, Karl H. Johansson, Dalton Jones, Mustafa Khammash, Pramod Khargonekar, Mykel J. Kochenderfer, Andreas Krause, Anthony Kuh, Dana Kulić, Françoise Lamnabhi-Lagarrigue, Naomi E. Leonard, Frederick Leve, Na Li, Steven Low, John Lygeros, Iven Mareels, Sonia Martinez, Nikolai Matni, Tommaso Menara, Katja Mombaur, Kevin Moore, Richard Murray, Toru Namerikawa, Angelia Nedich, Sandeep Neema, Mariana Netto, Timothy O’Leary, Marcia K. O’Malley, Lucy Y. Pao, Antonis Papachristodoulou, George J. Pappas, Philip E. Paré, Thomas Parisini, Fabio Pasqualetti, Marco Pavone, Akshay Rajhans, Gireeja Ranade, Anders Rantzer, Lillian Ratliff, J. Anthony Rossiter, Dorsa Sadigh, Tariq Samad, Henrik Sandberg, Sri Sarma, Luca Schenato, Jacquelien Scherpen, Angela Schoellig, Rodolphe Sepulchre, Jeff Shamma, Robert Shorten, Bruno Sinopoli, Koushil Sreenath, Jakob Stoustrup, Jing Sun, Paulo Tabuada, Emma Tegling, Dawn Tilbury, Claire J. Tomlin, Jana Tumova, Kevin Wise, Dan Work, Junaid Zafar, Melanie ZeilingerThis video was produced at the University of Washington
[2/8] Control for Societal-Scale Challenges: Road Map 2030 [Societal Drivers] Steve Brunton 2023-06-30 | This video explores the key societal drivers and challenges that face the world where control theory has a central role in future solutions. Examples include climate change, health and well being, smart infrastructure, and the sharing economy. This follows Chapter 2 of the "Control for Societal-Scale Challenges: Road Map 2030" document. ieeecss.org/control-societal-scale-challenges-road-map-2030The production of this video was supported by the IFAC. Roadmap Abstract: The world faces some of its greatest challenges of modern time and how we address them will have a dramatic impact on the life for generations to come. Simultaneously, control systems, consisting of information enriched by various degrees of analytics followed by decision-making, are pervading a variety of sectors, not only in engineering but beyond, into financial services, socio-economic analysis, entertainment and sports, and political and social sciences. Increased levels of automation are sought after in various sectors and being introduced into new domains. All of these advances and transformations urge a shift in the conversation toward how control systems can meet grand societal- scale challenges. The document seeks to chart a roadmap for the evolution of control systems, identifying several areas where our discipline can have an impact over the next decade.Edited by: Anuradha M. Annaswamy, Karl H. Johansson, and George J. PappasAuthors: Andrew Alleyne, Frank Allgöwer, Aaron D. Ames, Saurabh Amin, James Anderson, Anuradha M. Annaswamy, Panos J. Antsaklis, Neda Bagheri, Hamsa Balakrishnan, Bassam Bamieh, John Baras, Margret Bauer, Alexandre Bayen, Paul Bogdan, Steven L. Brunton, Francesco Bullo, Etienne Burdet, Joel Burdick, Laurent Burlion, Carlos Canudas de Wit, Ming Cao, Christos G. Cassandras, Aranya Chakrabortty, Giacomo Como, Marie Csete, Fabrizio Dabbene, Munther Dahleh, Amritam Das, Eyal Dassau, Claudio De Persis, Mario di Bernardo, Stefano Di Cairano, Dimos V. Dimarogonas, Florian Dörfler, John C. Doyle, Francis J. Doyle III, Anca Dragan, Magnus Egerstedt, Johan Eker, Sarah Fay, Dimitar Filev, Angela Fontan, Elisa Franco, Masayuki Fujita, Mario Garcia-Sanz, Dennice Gayme, Wilhelmus P.M.H. Heemels, João P. Hespanha, Sandra Hirche, Anette Hosoi, Jonathan P. How, Gabriela Hug, Marija Ilić, Hideaki Ishii, Ali Jadbabaie, Matin Jafarian, Samuel Qing-Shan Jia, Tor Arne Johansen, Karl H. Johansson, Dalton Jones, Mustafa Khammash, Pramod Khargonekar, Mykel J. Kochenderfer, Andreas Krause, Anthony Kuh, Dana Kulić, Françoise Lamnabhi-Lagarrigue, Naomi E. Leonard, Frederick Leve, Na Li, Steven Low, John Lygeros, Iven Mareels, Sonia Martinez, Nikolai Matni, Tommaso Menara, Katja Mombaur, Kevin Moore, Richard Murray, Toru Namerikawa, Angelia Nedich, Sandeep Neema, Mariana Netto, Timothy O’Leary, Marcia K. O’Malley, Lucy Y. Pao, Antonis Papachristodoulou, George J. Pappas, Philip E. Paré, Thomas Parisini, Fabio Pasqualetti, Marco Pavone, Akshay Rajhans, Gireeja Ranade, Anders Rantzer, Lillian Ratliff, J. Anthony Rossiter, Dorsa Sadigh, Tariq Samad, Henrik Sandberg, Sri Sarma, Luca Schenato, Jacquelien Scherpen, Angela Schoellig, Rodolphe Sepulchre, Jeff Shamma, Robert Shorten, Bruno Sinopoli, Koushil Sreenath, Jakob Stoustrup, Jing Sun, Paulo Tabuada, Emma Tegling, Dawn Tilbury, Claire J. Tomlin, Jana Tumova, Kevin Wise, Dan Work, Junaid Zafar, Melanie ZeilingerThis video was produced at the University of Washington
[1/8] Control for Societal-Scale Challenges: Road Map 2030 [Introduction] Steve Brunton 2023-06-30 | This video provides a high level introduction for this new road map document outlining some of the biggest societal challenges we face, and how control theory will be central in helping address them. ieeecss.org/control-societal-scale-challenges-road-map-2030The production of this video was supported by the IFAC. Roadmap Abstract: The world faces some of its greatest challenges of modern time and how we address them will have a dramatic impact on the life for generations to come. Simultaneously, control systems, consisting of information enriched by various degrees of analytics followed by decision-making, are pervading a variety of sectors, not only in engineering but beyond, into financial services, socio-economic analysis, entertainment and sports, and political and social sciences. Increased levels of automation are sought after in various sectors and being introduced into new domains. All of these advances and transformations urge a shift in the conversation toward how control systems can meet grand societal- scale challenges. The document seeks to chart a roadmap for the evolution of control systems, identifying several areas where our discipline can have an impact over the next decade.Edited by: Anuradha M. Annaswamy, Karl H. Johansson, and George J. PappasAuthors: Andrew Alleyne, Frank Allgöwer, Aaron D. Ames, Saurabh Amin, James Anderson, Anuradha M. Annaswamy, Panos J. Antsaklis, Neda Bagheri, Hamsa Balakrishnan, Bassam Bamieh, John Baras, Margret Bauer, Alexandre Bayen, Paul Bogdan, Steven L. Brunton, Francesco Bullo, Etienne Burdet, Joel Burdick, Laurent Burlion, Carlos Canudas de Wit, Ming Cao, Christos G. Cassandras, Aranya Chakrabortty, Giacomo Como, Marie Csete, Fabrizio Dabbene, Munther Dahleh, Amritam Das, Eyal Dassau, Claudio De Persis, Mario di Bernardo, Stefano Di Cairano, Dimos V. Dimarogonas, Florian Dörfler, John C. Doyle, Francis J. Doyle III, Anca Dragan, Magnus Egerstedt, Johan Eker, Sarah Fay, Dimitar Filev, Angela Fontan, Elisa Franco, Masayuki Fujita, Mario Garcia-Sanz, Dennice Gayme, Wilhelmus P.M.H. Heemels, João P. Hespanha, Sandra Hirche, Anette Hosoi, Jonathan P. How, Gabriela Hug, Marija Ilić, Hideaki Ishii, Ali Jadbabaie, Matin Jafarian, Samuel Qing-Shan Jia, Tor Arne Johansen, Karl H. Johansson, Dalton Jones, Mustafa Khammash, Pramod Khargonekar, Mykel J. Kochenderfer, Andreas Krause, Anthony Kuh, Dana Kulić, Françoise Lamnabhi-Lagarrigue, Naomi E. Leonard, Frederick Leve, Na Li, Steven Low, John Lygeros, Iven Mareels, Sonia Martinez, Nikolai Matni, Tommaso Menara, Katja Mombaur, Kevin Moore, Richard Murray, Toru Namerikawa, Angelia Nedich, Sandeep Neema, Mariana Netto, Timothy O’Leary, Marcia K. O’Malley, Lucy Y. Pao, Antonis Papachristodoulou, George J. Pappas, Philip E. Paré, Thomas Parisini, Fabio Pasqualetti, Marco Pavone, Akshay Rajhans, Gireeja Ranade, Anders Rantzer, Lillian Ratliff, J. Anthony Rossiter, Dorsa Sadigh, Tariq Samad, Henrik Sandberg, Sri Sarma, Luca Schenato, Jacquelien Scherpen, Angela Schoellig, Rodolphe Sepulchre, Jeff Shamma, Robert Shorten, Bruno Sinopoli, Koushil Sreenath, Jakob Stoustrup, Jing Sun, Paulo Tabuada, Emma Tegling, Dawn Tilbury, Claire J. Tomlin, Jana Tumova, Kevin Wise, Dan Work, Junaid Zafar, Melanie ZeilingerThis video was produced at the University of Washington
[6/8] Control for Societal-Scale Challenges: Road Map 2030 [Education] Steve Brunton 2023-06-30 | This video explores the importance of education in realizing the ambitious goals of this road map document. This follows Chapter 6 of the "Control for Societal-Scale Challenges: Road Map 2030" document. ieeecss.org/control-societal-scale-challenges-road-map-2030The production of this video was supported by the IFAC. Roadmap Abstract: The world faces some of its greatest challenges of modern time and how we address them will have a dramatic impact on the life for generations to come. Simultaneously, control systems, consisting of information enriched by various degrees of analytics followed by decision-making, are pervading a variety of sectors, not only in engineering but beyond, into financial services, socio-economic analysis, entertainment and sports, and political and social sciences. Increased levels of automation are sought after in various sectors and being introduced into new domains. All of these advances and transformations urge a shift in the conversation toward how control systems can meet grand societal- scale challenges. The document seeks to chart a roadmap for the evolution of control systems, identifying several areas where our discipline can have an impact over the next decade.Edited by: Anuradha M. Annaswamy, Karl H. Johansson, and George J. PappasAuthors: Andrew Alleyne, Frank Allgöwer, Aaron D. Ames, Saurabh Amin, James Anderson, Anuradha M. Annaswamy, Panos J. Antsaklis, Neda Bagheri, Hamsa Balakrishnan, Bassam Bamieh, John Baras, Margret Bauer, Alexandre Bayen, Paul Bogdan, Steven L. Brunton, Francesco Bullo, Etienne Burdet, Joel Burdick, Laurent Burlion, Carlos Canudas de Wit, Ming Cao, Christos G. Cassandras, Aranya Chakrabortty, Giacomo Como, Marie Csete, Fabrizio Dabbene, Munther Dahleh, Amritam Das, Eyal Dassau, Claudio De Persis, Mario di Bernardo, Stefano Di Cairano, Dimos V. Dimarogonas, Florian Dörfler, John C. Doyle, Francis J. Doyle III, Anca Dragan, Magnus Egerstedt, Johan Eker, Sarah Fay, Dimitar Filev, Angela Fontan, Elisa Franco, Masayuki Fujita, Mario Garcia-Sanz, Dennice Gayme, Wilhelmus P.M.H. Heemels, João P. Hespanha, Sandra Hirche, Anette Hosoi, Jonathan P. How, Gabriela Hug, Marija Ilić, Hideaki Ishii, Ali Jadbabaie, Matin Jafarian, Samuel Qing-Shan Jia, Tor Arne Johansen, Karl H. Johansson, Dalton Jones, Mustafa Khammash, Pramod Khargonekar, Mykel J. Kochenderfer, Andreas Krause, Anthony Kuh, Dana Kulić, Françoise Lamnabhi-Lagarrigue, Naomi E. Leonard, Frederick Leve, Na Li, Steven Low, John Lygeros, Iven Mareels, Sonia Martinez, Nikolai Matni, Tommaso Menara, Katja Mombaur, Kevin Moore, Richard Murray, Toru Namerikawa, Angelia Nedich, Sandeep Neema, Mariana Netto, Timothy O’Leary, Marcia K. O’Malley, Lucy Y. Pao, Antonis Papachristodoulou, George J. Pappas, Philip E. Paré, Thomas Parisini, Fabio Pasqualetti, Marco Pavone, Akshay Rajhans, Gireeja Ranade, Anders Rantzer, Lillian Ratliff, J. Anthony Rossiter, Dorsa Sadigh, Tariq Samad, Henrik Sandberg, Sri Sarma, Luca Schenato, Jacquelien Scherpen, Angela Schoellig, Rodolphe Sepulchre, Jeff Shamma, Robert Shorten, Bruno Sinopoli, Koushil Sreenath, Jakob Stoustrup, Jing Sun, Paulo Tabuada, Emma Tegling, Dawn Tilbury, Claire J. Tomlin, Jana Tumova, Kevin Wise, Dan Work, Junaid Zafar, Melanie ZeilingerThis video was produced at the University of Washington
[3/8] Control for Societal-Scale Challenges: Road Map 2030 [Technological Trends] Steve Brunton 2023-06-30 | This video explores the broad technological trends that are advancing our capabilities, including AI and big data, the trend towards electrification, engineering biology, and the ubiquity of robots in the real world. This follows Chapter 3 of the "Control for Societal-Scale Challenges: Road Map 2030" document. ieeecss.org/control-societal-scale-challenges-road-map-2030The production of this video was supported by the IFAC. Roadmap Abstract: The world faces some of its greatest challenges of modern time and how we address them will have a dramatic impact on the life for generations to come. Simultaneously, control systems, consisting of information enriched by various degrees of analytics followed by decision-making, are pervading a variety of sectors, not only in engineering but beyond, into financial services, socio-economic analysis, entertainment and sports, and political and social sciences. Increased levels of automation are sought after in various sectors and being introduced into new domains. All of these advances and transformations urge a shift in the conversation toward how control systems can meet grand societal- scale challenges. The document seeks to chart a roadmap for the evolution of control systems, identifying several areas where our discipline can have an impact over the next decade.Edited by: Anuradha M. Annaswamy, Karl H. Johansson, and George J. PappasAuthors: Andrew Alleyne, Frank Allgöwer, Aaron D. Ames, Saurabh Amin, James Anderson, Anuradha M. Annaswamy, Panos J. Antsaklis, Neda Bagheri, Hamsa Balakrishnan, Bassam Bamieh, John Baras, Margret Bauer, Alexandre Bayen, Paul Bogdan, Steven L. Brunton, Francesco Bullo, Etienne Burdet, Joel Burdick, Laurent Burlion, Carlos Canudas de Wit, Ming Cao, Christos G. Cassandras, Aranya Chakrabortty, Giacomo Como, Marie Csete, Fabrizio Dabbene, Munther Dahleh, Amritam Das, Eyal Dassau, Claudio De Persis, Mario di Bernardo, Stefano Di Cairano, Dimos V. Dimarogonas, Florian Dörfler, John C. Doyle, Francis J. Doyle III, Anca Dragan, Magnus Egerstedt, Johan Eker, Sarah Fay, Dimitar Filev, Angela Fontan, Elisa Franco, Masayuki Fujita, Mario Garcia-Sanz, Dennice Gayme, Wilhelmus P.M.H. Heemels, João P. Hespanha, Sandra Hirche, Anette Hosoi, Jonathan P. How, Gabriela Hug, Marija Ilić, Hideaki Ishii, Ali Jadbabaie, Matin Jafarian, Samuel Qing-Shan Jia, Tor Arne Johansen, Karl H. Johansson, Dalton Jones, Mustafa Khammash, Pramod Khargonekar, Mykel J. Kochenderfer, Andreas Krause, Anthony Kuh, Dana Kulić, Françoise Lamnabhi-Lagarrigue, Naomi E. Leonard, Frederick Leve, Na Li, Steven Low, John Lygeros, Iven Mareels, Sonia Martinez, Nikolai Matni, Tommaso Menara, Katja Mombaur, Kevin Moore, Richard Murray, Toru Namerikawa, Angelia Nedich, Sandeep Neema, Mariana Netto, Timothy O’Leary, Marcia K. O’Malley, Lucy Y. Pao, Antonis Papachristodoulou, George J. Pappas, Philip E. Paré, Thomas Parisini, Fabio Pasqualetti, Marco Pavone, Akshay Rajhans, Gireeja Ranade, Anders Rantzer, Lillian Ratliff, J. Anthony Rossiter, Dorsa Sadigh, Tariq Samad, Henrik Sandberg, Sri Sarma, Luca Schenato, Jacquelien Scherpen, Angela Schoellig, Rodolphe Sepulchre, Jeff Shamma, Robert Shorten, Bruno Sinopoli, Koushil Sreenath, Jakob Stoustrup, Jing Sun, Paulo Tabuada, Emma Tegling, Dawn Tilbury, Claire J. Tomlin, Jana Tumova, Kevin Wise, Dan Work, Junaid Zafar, Melanie ZeilingerThis video was produced at the University of Washington
[4/8] Control for Societal-Scale Challenges: Road Map 2030 [Emerging Methodologies] Steve Brunton 2023-06-30 | This video explores the emerging methodologies in control theory that are rapidly improving our capabilities to solve societal scale challenges. These methods include learning and data-driven control, safety-critical systems, resilient cyber physical systems, cyber-physical human systems, and control architectures. This follows Chapter 4 of the "Control for Societal-Scale Challenges: Road Map 2030" document. ieeecss.org/control-societal-scale-challenges-road-map-2030The production of this video was supported by the IFAC. Roadmap Abstract: The world faces some of its greatest challenges of modern time and how we address them will have a dramatic impact on the life for generations to come. Simultaneously, control systems, consisting of information enriched by various degrees of analytics followed by decision-making, are pervading a variety of sectors, not only in engineering but beyond, into financial services, socio-economic analysis, entertainment and sports, and political and social sciences. Increased levels of automation are sought after in various sectors and being introduced into new domains. All of these advances and transformations urge a shift in the conversation toward how control systems can meet grand societal- scale challenges. The document seeks to chart a roadmap for the evolution of control systems, identifying several areas where our discipline can have an impact over the next decade.Edited by: Anuradha M. Annaswamy, Karl H. Johansson, and George J. PappasAuthors: Andrew Alleyne, Frank Allgöwer, Aaron D. Ames, Saurabh Amin, James Anderson, Anuradha M. Annaswamy, Panos J. Antsaklis, Neda Bagheri, Hamsa Balakrishnan, Bassam Bamieh, John Baras, Margret Bauer, Alexandre Bayen, Paul Bogdan, Steven L. Brunton, Francesco Bullo, Etienne Burdet, Joel Burdick, Laurent Burlion, Carlos Canudas de Wit, Ming Cao, Christos G. Cassandras, Aranya Chakrabortty, Giacomo Como, Marie Csete, Fabrizio Dabbene, Munther Dahleh, Amritam Das, Eyal Dassau, Claudio De Persis, Mario di Bernardo, Stefano Di Cairano, Dimos V. Dimarogonas, Florian Dörfler, John C. Doyle, Francis J. Doyle III, Anca Dragan, Magnus Egerstedt, Johan Eker, Sarah Fay, Dimitar Filev, Angela Fontan, Elisa Franco, Masayuki Fujita, Mario Garcia-Sanz, Dennice Gayme, Wilhelmus P.M.H. Heemels, João P. Hespanha, Sandra Hirche, Anette Hosoi, Jonathan P. How, Gabriela Hug, Marija Ilić, Hideaki Ishii, Ali Jadbabaie, Matin Jafarian, Samuel Qing-Shan Jia, Tor Arne Johansen, Karl H. Johansson, Dalton Jones, Mustafa Khammash, Pramod Khargonekar, Mykel J. Kochenderfer, Andreas Krause, Anthony Kuh, Dana Kulić, Françoise Lamnabhi-Lagarrigue, Naomi E. Leonard, Frederick Leve, Na Li, Steven Low, John Lygeros, Iven Mareels, Sonia Martinez, Nikolai Matni, Tommaso Menara, Katja Mombaur, Kevin Moore, Richard Murray, Toru Namerikawa, Angelia Nedich, Sandeep Neema, Mariana Netto, Timothy O’Leary, Marcia K. O’Malley, Lucy Y. Pao, Antonis Papachristodoulou, George J. Pappas, Philip E. Paré, Thomas Parisini, Fabio Pasqualetti, Marco Pavone, Akshay Rajhans, Gireeja Ranade, Anders Rantzer, Lillian Ratliff, J. Anthony Rossiter, Dorsa Sadigh, Tariq Samad, Henrik Sandberg, Sri Sarma, Luca Schenato, Jacquelien Scherpen, Angela Schoellig, Rodolphe Sepulchre, Jeff Shamma, Robert Shorten, Bruno Sinopoli, Koushil Sreenath, Jakob Stoustrup, Jing Sun, Paulo Tabuada, Emma Tegling, Dawn Tilbury, Claire J. Tomlin, Jana Tumova, Kevin Wise, Dan Work, Junaid Zafar, Melanie ZeilingerThis video was produced at the University of Washington
[7/8] Control for Societal-Scale Challenges: Road Map 2030 [Ethics, Fairness, & Regulatory Issues] Steve Brunton 2023-06-30 | This video explores ethics, fairness, and regulatory issues associated with applying control to solve these major societal challenges. This follows Chapter 7 of the "Control for Societal-Scale Challenges: Road Map 2030" document. ieeecss.org/control-societal-scale-challenges-road-map-2030The production of this video was supported by the IFAC. Roadmap Abstract: The world faces some of its greatest challenges of modern time and how we address them will have a dramatic impact on the life for generations to come. Simultaneously, control systems, consisting of information enriched by various degrees of analytics followed by decision-making, are pervading a variety of sectors, not only in engineering but beyond, into financial services, socio-economic analysis, entertainment and sports, and political and social sciences. Increased levels of automation are sought after in various sectors and being introduced into new domains. All of these advances and transformations urge a shift in the conversation toward how control systems can meet grand societal- scale challenges. The document seeks to chart a roadmap for the evolution of control systems, identifying several areas where our discipline can have an impact over the next decade.Edited by: Anuradha M. Annaswamy, Karl H. Johansson, and George J. PappasAuthors: Andrew Alleyne, Frank Allgöwer, Aaron D. Ames, Saurabh Amin, James Anderson, Anuradha M. Annaswamy, Panos J. Antsaklis, Neda Bagheri, Hamsa Balakrishnan, Bassam Bamieh, John Baras, Margret Bauer, Alexandre Bayen, Paul Bogdan, Steven L. Brunton, Francesco Bullo, Etienne Burdet, Joel Burdick, Laurent Burlion, Carlos Canudas de Wit, Ming Cao, Christos G. Cassandras, Aranya Chakrabortty, Giacomo Como, Marie Csete, Fabrizio Dabbene, Munther Dahleh, Amritam Das, Eyal Dassau, Claudio De Persis, Mario di Bernardo, Stefano Di Cairano, Dimos V. Dimarogonas, Florian Dörfler, John C. Doyle, Francis J. Doyle III, Anca Dragan, Magnus Egerstedt, Johan Eker, Sarah Fay, Dimitar Filev, Angela Fontan, Elisa Franco, Masayuki Fujita, Mario Garcia-Sanz, Dennice Gayme, Wilhelmus P.M.H. Heemels, João P. Hespanha, Sandra Hirche, Anette Hosoi, Jonathan P. How, Gabriela Hug, Marija Ilić, Hideaki Ishii, Ali Jadbabaie, Matin Jafarian, Samuel Qing-Shan Jia, Tor Arne Johansen, Karl H. Johansson, Dalton Jones, Mustafa Khammash, Pramod Khargonekar, Mykel J. Kochenderfer, Andreas Krause, Anthony Kuh, Dana Kulić, Françoise Lamnabhi-Lagarrigue, Naomi E. Leonard, Frederick Leve, Na Li, Steven Low, John Lygeros, Iven Mareels, Sonia Martinez, Nikolai Matni, Tommaso Menara, Katja Mombaur, Kevin Moore, Richard Murray, Toru Namerikawa, Angelia Nedich, Sandeep Neema, Mariana Netto, Timothy O’Leary, Marcia K. O’Malley, Lucy Y. Pao, Antonis Papachristodoulou, George J. Pappas, Philip E. Paré, Thomas Parisini, Fabio Pasqualetti, Marco Pavone, Akshay Rajhans, Gireeja Ranade, Anders Rantzer, Lillian Ratliff, J. Anthony Rossiter, Dorsa Sadigh, Tariq Samad, Henrik Sandberg, Sri Sarma, Luca Schenato, Jacquelien Scherpen, Angela Schoellig, Rodolphe Sepulchre, Jeff Shamma, Robert Shorten, Bruno Sinopoli, Koushil Sreenath, Jakob Stoustrup, Jing Sun, Paulo Tabuada, Emma Tegling, Dawn Tilbury, Claire J. Tomlin, Jana Tumova, Kevin Wise, Dan Work, Junaid Zafar, Melanie ZeilingerThis video was produced at the University of Washington
🤯🤯🤯 Integrating GPT-4 into Collimator.ai for Symbolic Modeling and Control** 🔥🔥🔥 Steve Brunton 2023-06-23 | In this video, we explore how @collimator uses GPT-4 to bring generative AI into their modeling and control framework. Stunning results. **Please note: this feature is currently available in our private beta. public release is coming soon, stay tuned!Will be possible to design and analyze control systems symbolically from text prompts soon!Check out Collimator and control smarter! 🧠🧠🧠 collimator.ai
New Book!!! Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control Steve Brunton 2023-06-16 | New 2nd Edition of our book: "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan KutzDOWNLOAD 2ND ED PDF: https://faculty.washington.edu/sbrunton/DataBookV2.pdf1ST ED PDF: databookuw.com/databook.pdfAMAZON: amazon.com/Data-Driven-Science-Engineering-Learning-Dynamical-dp-1009098489/dp/1009098489/ref=dp_ob_title_bkCAMBRIDGE: cambridge.org/highereducation/books/data-driven-science-and-engineering/6F9A730B7A9A9F43F68CF21A24BEC339#overview CODES: github.com/dynamicslabNEW IN THIS EDITION: * New Chapters: * Reinforcement learning * Physics-informed machine learning * Code in Python and Matlab * Homework for every chapter ranging from introductory topics to advanced projects * Videos for every section * New sections throughout, with topics including condition number and error bounds for the SVD; autoencoders, recurrent neural networks, and generative adversarial networks; and neural networks for reduced-order modelsData-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material – including lecture videos per section, homeworks, data, and code in MATLAB®, Python, Julia, and R – available on databookuw.com.
Complex Analysis L13: Bromwich Integrals and the Inverse Laplace Transform Steve Brunton 2023-04-21 | This video is a culmination of this series on complex analysis, where we show how to compute the Bromwich integral used in the inverse Laplace transform. @eigensteve on Twittereigensteve.comdatabookuw.comThis video was produced at the University of Washington
Complex Analysis L12: Examples of Complex Integrals Steve Brunton 2023-04-14 | This video presents examples of how to use the various complex integration theorems to compute challenging complex integrals. @eigensteve on Twittereigensteve.comdatabookuw.comThis video was produced at the University of Washington
Complex analysis 101: imaginary numbers are real! Steve Brunton 2023-04-09 | Check out my complete series on complex analysis!
Complex Analysis L11: Examples of Cauchy-Integral Formula Steve Brunton 2023-04-07 | This video explores examples of the Cauchy integral formula for contour integrals in the complex plane. @eigensteve on Twittereigensteve.comdatabookuw.comThis video was produced at the University of Washington
Complex Analysis L10: Cauchy Integral Formula Steve Brunton 2023-03-31 | This video explores the Cauchy Integral Formula (CIF), which is one of the most important theorems for complex contour integrals. @eigensteve on Twittereigensteve.comdatabookuw.comThis video was produced at the University of Washington
Complex Analysis L09: Complex Residues Steve Brunton 2023-03-24 | This video discusses the residue theorem in complex analysis and how to compute complex contour integrals around singular points. This culminates in the integral of the function f(z)=1/z.@eigensteve on Twittereigensteve.comdatabookuw.comThis video was produced at the University of Washington
Complex Analysis L08: Integrals in the Complex Plane Steve Brunton 2023-03-17 | This video explores contour integration of functions in the complex plane. @eigensteve on Twittereigensteve.comdatabookuw.comThis video was produced at the University of Washington
Complex Analysis L07: Analytic Functions Solve Laplaces Equation Steve Brunton 2023-03-10 | This video shows that the real and imaginary parts of analytic complex functions solve Laplace's equation. These are known as harmonic functions. @eigensteve on Twittereigensteve.comdatabookuw.com
Complex Analysis L06: Analytic Functions and Cauchy-Riemann Conditions Steve Brunton 2023-03-03 | This video explores analytic complex functions, where it is possible to do calculus. We introduce the Cauchy-Riemann conditions to test for analyticity. @eigensteve on Twittereigensteve.comdatabookuw.comThis video was produced at the University of Washington
Complex Analysis L05: Roots of Unity and Rational Powers of z Steve Brunton 2023-02-24 | This video explains how to use the complex Logarithm, Log(z), and the exponential to compute fractional/rational powers of complex numbers. A special case are the n-th roots of the number 1, or the square root of i, etc... @eigensteve on Twittereigensteve.comdatabookuw.comThis video was produced at the University of Washington
Complex Analysis L04: The Complex Logarithm, Log(z) Steve Brunton 2023-02-17 | This video introduces the complex Logarithm, Log(z), as the inverse of the complex exponential. The Logarithm is a very important function that has infinitely many values in the complex plane. We also discuss branch cuts, and principle n-th roots. @eigensteve on Twittereigensteve.comdatabookuw.comThis video was produced at the University of Washington%%% CHAPTERS %%%0:00 Defining the complex Logarithm11:58 Plotting the complex Logarithm16:26 Full formula for Log(z)18:54 Recap/Summary21:37 Branch cuts23:12 Infinite spiral staircase of solutions25:28 Teaser: Cauchy Integral Formula
Complex Analysis L03: Functions of a complex variable, f(z) Steve Brunton 2023-02-10 | This video introduces functions of a complex variable, f(z). Important functions include monomials z^n, the complex exponential exp(z), trigonometric functions sin(z), cos(z), among others. The complex Logarithm will be the subject of the next lecture. @eigensteve on Twittereigensteve.comdatabookuw.comThis video was produced at the University of Washington%%% CHAPTERS %%%0:00 Complex functions: Overview and examples9:34 The convergent Taylor series of the complex exponential13:41 Monomial functions z^n 18:03 Trigonometric functions sin(z), cos(z) and identities
Complex Analysis L02: Eulers formula, one of the most important formulas in all of mathematics Steve Brunton 2023-02-03 | This video derives Euler's formula (one of the most important formulas in all of mathematics!!!) from the Taylor series of the complex exponential exp(i*theta). @eigensteve on Twittereigensteve.comdatabookuw.comThis video was produced at the University of Washington%%% CHAPTERS %%%0:00 Euler's formula and the Taylor series of exp(i theta)10:49 e^(i pi) = -113:19 DeMoivre's Formula
Complex Analysis L01: Overview & Motivation, Complex Arithmetic, Eulers Formula & Polar Coordinates Steve Brunton 2023-01-27 | This is the first overview lecture in a new short-course on complex analysis. Here we motivate and introduce complex numbers z=x+iy, discuss how they are solutions to differential equations, and explain how to perform basic arithmetic, such as addition, subtraction, multiplication, and division. We also introduce Euler's formula and complex numbers in polar coordinates.@eigensteve on Twittereigensteve.comdatabookuw.comThis video was produced at the University of Washington%%% CHAPTERS %%%0:00 Introduction and motivation10:00 Euler's formula11:27 Complex addition, subtraction, multiplication, and division17:33 Complex numbers in polar coordinates: Radius and phase angle26:16 Where this is going
Topics in Dynamical Systems: Fixed Points, Linearization, Invariant Manifolds, Bifurcations & Chaos Steve Brunton 2023-01-16 | This video provides a high-level overview of dynamical systems, which describe the changing world around us. Topics include nonlinear dynamics, linearization at fixed points, eigenvalues and eigenvectors, bifurcations, invariant manifolds, and chaos!!@eigensteve on Twittereigensteve.comdatabookuw.comThis video was produced at the University of Washington%%% CHAPTERS %%%0:00 Introduction4:35 Linearization at a Fixed Point9:37 Why We Linearize: Eigenvalues and Eigenvectors14:46 Nonlinear Example: The Duffing Equation19:59 Stable and Unstable Manifolds21:12 Bifurcations25:20 Discrete-Time Dynamics: Population Dynamics27:02 Integrating Dynamical System Trajectories29:07 Chaos and Mixing
Solving PDEs with the Laplace Transform: The Wave Equation Steve Brunton 2023-01-13 | This video shows how to solve Partial Differential Equations (PDEs) with Laplace Transforms. Specifically we solve the wave equation on a semi-infinite domain. @eigensteve on Twittereigensteve.comdatabookuw.comThis video was produced at the University of Washington%%% CHAPTERS %%%0:00 Overview and Problem Setup (Initial Conditions and Boundary Conditions) 3:46 Laplace Transform in Time: PDE to ODE8:20 Solving the ODE in Space12:57 General Solution of the Wave Equation15:38 The Heaviside Function19:36 Illustration and Method of Characteristics
Solving PDEs with the Laplace Transform: The Heat Equation Steve Brunton 2023-01-09 | This video shows how to solve Partial Differential Equations (PDEs) with Laplace Transforms. Specifically we solve the heat equation on a semi-infinite domain. @eigensteve on Twittereigensteve.comdatabookuw.comThis video was produced at the University of Washington%%% CHAPTERS %%%0:00 Overview and Problem Setup 7:03 How Classic Methods (e.g., Laplace) Relate to Modern Problems9:17 Laplace Transform with respect to Time15:10 Solving ODE with Forcing: Homogeneous and Particular Solution19:12 The Particular Solution and Initial Conditions 28:20 The Homogeneous Solution and Boundary Conditions31:52 The Solution in Frequency and Time Domains
Moores Law, exponential growth, and extrapolation! Steve Brunton 2023-01-04 | Moore's law of exponential growth in computing is important for machine learning, scientific computing, computer graphics, and even for artificial intelligence. In this video we discuss the basis of Moore's law, and projections for the future.
