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

Uploaded February 2026 | Updated September 2026, 3 weeks ago

Recorded 03 February 2026. Dimitris Giannakis of Dartmouth College presents "Operator-theoretic approaches to feature extraction and statistical modeling of climate dynamics" at IPAM's Mathematics and Machine Learning for Earth System Simulation Workshop.
Abstract: Over the past three decades, operator-theoretic techniques have proven to be highly fruitful for analysis and data-driven modeling of dynamical systems. These methods leverage the linearity of the action of (nonlinear) dynamics on spaces of observables or probability distributions (induced by Koopman or transfer operators, respectively) to perform spectral decomposition, forecasting, and uncertainty quantification in complex systems. In this talk, we survey aspects of the mathematical formulation of data-driven dynamical operator methods and present applications to climate dynamics on daily to interannual timescales. Central to our approach are kernel methods for consistent operator approximation in both supervised and unsupervised learning problems. We illustrate these methods with applications to extraction and prediction of the El Nino Southern Oscillation and dynamical closure in cloud-resolving atmospheric models.
Learn more online at: https://www.ipam.ucla.edu/programs/workshops/mathematics-and-machine-learning-for-earth-system-simulation/?tab=overview

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

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

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