• 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 and computational 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

Precursors of extremes in directional water waves

MP JCP M. Farazmand, T. Sapsis, Reduced-order prediction of rogue waves in two dimensional water wavesJournal of Computational Physics340 (2017) 418-434. [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]

Precursors of extreme events in complex systems

MP SA  M. Farazmand, T. Sapsis, A variational approach for probing extreme events in complex systemsScience Advances, (2017) 3:e1701533. [pdf]

Sequential sampling for extreme event statistics

MP PNAS M. Mohamad, T. Sapsis, A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems, Proceedings of the National Academy of Sciences115 (2018) 11138-11143. [open access link]

Physics informed machine learning of complex systems

MP ML Z. Y. Wan, P. Vlachas, P. Koumoutsakos, T. Sapsis, Data-assisted reduced-order modeling of extreme events in complex dynamical systemsPLOS One, 24 May (2018) (22 pages). [pdf]