Peter Benner - Discovery and Model Reduction of Hamiltonian Systems

Uploaded April 2026 | Updated September 2026, 3 weeks ago

Recorded 14 April 2026. Peter Benner of the Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, presents "Discovery and Model Reduction of Hamiltonian Systems" at IPAM's Learning Models from Data for Multi-Fidelity Fusion Plasma Physics Workshop.
Abstract: One often encountered computational issue in studying nuclear fusion reactors is the simulation of the behavior of confined plasmas in a controlled magnetic field. Numerical modeling requires the solution of the Vlasov equation, potentially coupled to other partial differential equations. After spatial discretization, this leads to high-dimensional Hamiltonian systems. Fast simulation under varying conditions requires learning a compact, reduced-order model that is fast to simulate and faithfully reproduces the kinetics of the full-order model. We discuss several approaches to learn Hamiltonian systems from data. Model reduction can be achieved using variational autoencoders. We show how the underlying Hamiltonian structure, resulting in a symplectic flow field, can be preserved using symplectic convolutional neural networks. This talk will discuss joint research with Thomas Bendokat, Konrad Janik, Pawan K. Goyal, Igor Pontes Duff, and Süleyman Yildiz.
Learn more online at: https://www.ipam.ucla.edu/programs/workshops/workshop-ii-learning-models-from-data-for-multi-fidelity-fusion-plasma-physics/

Peter Benner - Discovery and Model Reduction of Hamiltonian Systems - IPAM at UCLA

John Jakeman - Forward Uncertainty Quantification - IPAM at UCLA

Paul Newton - Gaming the Cancer-Immunity Cycle by Synchronizing the Dose Schedules - IPAM at UCLA

Smita Krishnaswamy - Cellular Trajectories & Regulatory Networks w/ Neural & Graph ODE Models, 2/2

Max Tegmark - Neural network interpretability: symmetry, geometry and formal verification

Dimitris Giannakis - Operator-theoretic approach, feature extraction & statistical model of climate

$Artur Izmaylov - Quantum SENiority-based Subspace Expansion (Q-SENSE) and Its Extensions Recorded 18 February 2026. Artur Izmaylov of the University of Toronto presents Quantum SENiority-based Subspace Expansion (Q-SENSE) and Its Extensions at IPAMs Bridging the Gap Between NISQ and FTQC Workshop. Abstract: Quantum SENiority-based Subspace Expansion (Q-SENSE) is a hybrid quantum-classical algorithm that interpolates between the Variational Quantum Eigensolver (VQE) and Configuration Interaction (CI) methods. It constructs Hamiltonian matrix elements on a quantum device and solves the resulting eigenvalue problem classically. Unlike other expansion-based methods — such as Quantum Subspace Expansion (QSE), Quantum Krylov Algorithms, and the Non-Orthogonal Quantum Eigensolver — Q-SENSE introduces seniority operators as artificial symmetries to construct orthogonal basis states. This seniority-symmetry-based approach reduces one of the primary limitations of VQE on near-term quantum hardware — circuit depth — at the cost of measuring additional matrix elements. The artificial symmetries also reduce the number of Hamiltonian terms that must be measured, as only a small fraction of the terms couple basis states in different seniority subspaces. With all these merits, Q-SENSE offers a scalable and resource-efficient route to quantum advantage on near-term quantum devices and in the early fault-tolerant regime. Learn more online at: https://www.ipam.ucla.edu/programs/workshops/bridging-the-gap-between-nisq-and-ftqc/$

Ciaran Ryan-Anderson - Experimental Quantum Error Correction at Quantinuum - IPAM at UCLA

Zhiyan Ding - Ground State Preparation for Near-Term Devices using System-Bath Coupling Dynamics

Miguel Garcia-Garibay - What is being transformed by AI in Higher Education? (Everything)

Ionut-Gabriel Farcas - A tutorial on multi-fidelity methods for fusion energy research, Pt. 1/2

Sebastien Bubeck - A Combinatorics Problem - IPAM at UCLA