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Welcome to the research group of Themis Sapsis!

In the Stochastic Analysis and Nonlinear Dynamics (SAND) lab our goal is to understand, predict, and/or optimize complex engineering and environmental systems where uncertainty or stochasticity is equally important with the dynamics. We specialize on the development of analytical, computational and data-driven methods for modeling high-dimensional nonlinear systems characterized by nonlinear energy transfers between dynamical components, broad energy spectra with complex statistics, and persistent or intermittent instabilities. We are particularly interested on the development of inexpensive predictive capacity for such systems as well as the development of design criteria for engineering applications.

Recent papers

Optimal acquisition functions for active learning

converge T. Sapsis, A. Blanchard, Optimal criteria and their asymptotic form for data selection in data-driven reduced-order modeling with Gaussian process regression, Philosophical Transactions of the Royal Society A, (2022). [pdf]

Extreme events via active learning with neural operators

DNN E. Pickering, G. Karniadakis, T. Sapsis, Discovering and forecasting extreme events via active learning in neural operators, Submitted, (2022). [pdf]

Review: Statistics of Extreme Events in Fluid Flows and Waves

pic ARFM T. Sapsis, Statistics of extreme events in fluid flows and waves, Annual Review of Fluid Mechanics, 53, 85-111, (2021). [pdf]

Bayesian optimization with output-weighted optimal sampling

SIAM pic A. Blanchard, T. Sapsis, Bayesian optimization with output-weighted optimal sampling, Journal of Computational Physics, 425 (2021) 109901. [pdf]

Output-weighted optimal sampling for Bayesian regression and rare event statistics

PRSA0  T. Sapsis, Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples, Proceedings of the Royal Society A, 476 (2020) 20190834. [pdf]