Stochastic Closures and Data-Driven Modeling for Dynamical Systems

Stochastic closures for uncertainty quantification

  • A. Charalampopoulos, T. Sapsis, Machine-learning energy-preserving nonlocal closures for turbulent fluid flows and inertial tracers, Submitted, (2021) (25 pages). [pdf]
  • H. -K. Joo, T. Sapsis, A moment-equation-copula-closure method for nonlinear vibrational systems subjected to correlated noiseProbabilistic Engineering Mechanics46 (2016) 120-132. [pdf]
  • T. Sapsis, A. Majda, A statistically accurate modified quasilinar Gaussian closure for uncertainty quantification in turbulent dynamical systemsPhysica D252 (2013) 34-45. [pdf]
  • D. Venturi, T. Sapsis, H. Cho, and G. E. Karniadakis, A computable evolution equation for the joint response-excitation probability density function of stochastic dynamical systemsProceedings of the Royal Society A468 (2012) 759 (25 pages). [pdf]
  • T. Sapsis & G. Athanassoulis, New partial differential equations governing the response-excitation joint probability distributions of nonlinear systems under general stochastic excitationProbabilistic Engineering Mechanics23 (2008) 289-306. [pdf]

Data-driven modeling for prediction

  • S. Rudy, T. Sapsis, Prediction of intermittent fluctuations from surface pressure measurements on a turbulent airfoilSubmitted (2021). [pdf]
  • D. Eeltink, H. Branger, C. Luneau, J. Kasparian, T. S. van den Bremer, T. Sapsis, Nonlinear wave evolution with data-driven breakingSubmitted (2021). [pdf]
  • S. Rudy, D. Fan, J. Ferrandis, T. Sapsis, M. Triantafyllou, Optimized parametric hydrodynamic databases provide accurate response predictions and describe the physics of vortex-induced vibrationsSubmitted (2021). [pdf]
  • P. Vlachas, J. Pathak, B. R. Hunt, T. Sapsis, M. Girvan, E. Ott, P. Koumoutsakos, Forecasting of spatio-temporal chaotic dynamics with recurrent neural networks: a comparative study of reservoir computing and backpropagation algorithmsNeural Networks, 126 (2020) 191-217[pdf]
  • 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]
  • P. Vlachas, W. Byeon, Z. Y. Wan, T. Sapsis, P. Koumoutsakos, Data-driven forecasting of high-dimensional chaotic systems with long-short term memory networksProceedings of the Royal Society A474 (2018) 20170844 (20 pages). [pdf]
  • Z. Y. Wan, T. Sapsis, Reduced-space Gaussian process regression for data-driven probabilistic forecast of chaotic dynamical systemsPhysica D345 (2017) 40-55. [pdf]