• Fig FP EXTREMEa2
  • Fig FP TET
  • Fig FP ROMQGa
  • Fig FP DO1
  • Fig FP Jointa
  • Fig FP IP1
  • Long-term probabilistic quantification and short-term prediction of extreme waves
  • Targeted energy transfer in nonlinear oscillators with applications in energy harvesting and passive protection of structures
  • Statistical closure and reduced-order modeling of turbulent flows (ROMQG closure)
  • Stochastic attractors in low-dimensional, chaotic flows (DO method)
  • Probabilistic description of systems subjected to colored noise (Joint response-excitation method)
  • Mixing, clustering, and transport of finite size particles (bubbles and aerosols) in fluid flows

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

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 (2020). [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]

Stochastic machine-learned parametrization of small scales in climate models

Smal Z. Wan, B. Dodov, C. Lessig, H. Dijkstra, T. Sapsis, A data-driven framework for the stochastic reconstruction of small-scale features in climate data sets (2020). [pdf]

Precursors of extreme events in turbulence

MP PRF P. Blonigan, M. Farazmand, T. Sapsis, Are extreme dissipation events predictable in turbulent fluid flows?Physical Review Fluids, 4 (2019) 044606. [pdf]