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Bayesian Analysis and Optimal Experimental Design

  • A. Blanchard, T. Sapsis, Output-weighted optimal sampling for Bayesian experimental design and rare-event quantification, Submitted, (2020) (26 pages). [pdf]
  • S. Rudy, T. Sapsis, Sparse methods for automatic relevance determination, Submitted, (2020) (30 pages). [code] [pdf]
  • A. Blanchard, T. Sapsis, Informative path planning for anomaly detection in environment exploration and monitoring, Submitted, (2020) (19 pages). [movies] [pdf]
  • A. Blanchard, T. Sapsis, Bayesian optimization with output-weighted optimal sampling, Submitted, (2020) (22 pages). [code] [pdf]
  • 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 (24 pages). [pdf]
  • M. Mohamad, T. Sapsis, A sequential sampling strategy for extreme event statistics in nonlinear dynamical systems, Proceedings of the National Academy of Sciences, 115 (2018) 11138-11143. [open access link] [supporting information]
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Machine Learning for Physical Systems

  • Z. Wan, B. Dodov, C. Lessig, H. Dijkstra, T. Sapsis, A data-driven framework for the stochastic parametrization and reconstruction of small-scale features in climate models, Submitted, (2020) (27 pages). [Movie] [pdf]
  • H. Arbabi, T. Sapsis, Generative stochastic modeling of strongly nonlinear flows with non-Gaussian statisticsSubmitted, (2020) (32 pages). [code] [pdf]
  • Z. Y. Wan, P. Karnakov, P. Koumoutsakos, T. Sapsis, Bubbles in turbulent flows: Data-driven, kinematic models with history termsInternational Journal of Multiphase Flows, 129 (2020) 103286 (11 pages)[pdf]
  • N. Aksamit, T. Sapsis, G. Haller, Machine-learning ocean dynamics from Lagrangian drifter trajectoriesJournal of Physical Oceanography, 50 (2020) 1179-1196[pdf]
  • S. Bryngelson, A. Charalampopoulos, T. Sapsis, T. Colonius, A Gaussian moment method and its augmentation via LSTM recurrent neural networks for the statistics of cavitating bubble populationsInternational Journal of Multiphase Flows, 127 (2020) 103262 (8 pages)[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]
  • A. Blanchard, T. Sapsis, Learning the tangent space of dynamical instabilities from dataChaos29 (2019) 113120, Focus Issue: When Machine Learning Meets Complex Systems: Networks, Chaos and Nonlinear Dynamics(2019) (15 pages)[pdf]
  • S. Guth, T. Sapsis, Machine learning predictors of extreme events occurring in complex dynamical systemsEntropy21 (2019) 925 (18 pages). [pdf]
  • Z. Y. Wan, T. Sapsis, Machine learning the kinematics of spherical particles in fluid flowsJournal of Fluid Mechanics857 (2018) R2 (11 pages). [Code] [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]
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Uncertainty Quantification and Reduced Order Modeling of Fluid Flows

  • A. Blanchard, T. Sapsis, Learning the tangent space of dynamical instabilities from dataChaos29 (2019) 113120, Focus Issue: When Machine Learning Meets Complex Systems: Networks, Chaos and Nonlinear Dynamics(2019) (15 pages)[pdf]
  • A. Blanchard, T. Sapsis, Analytical description of optimally time-dependent modes for reduced-order modeling of transient instabilitiesSIAM Journal on Applied Dynamical Systems, 18 (2019) 1143-1162. [pdf]
  • S. Mowlavi, T. Sapsis, Model order reduction for stochastic dynamical systems with continuous symmetriesSIAM Journal on Scientific Computing40 (2018) 1669-1695. [pdf]
  • H. BabaeeM. Farazmand, G. Haller, T. Sapsis, Reduced-order description of transient instabilities and computation of finite-time Lyapunov exponentsChaos27 (2017) 063103 (12 pages). [pdf]
  • H. Babaee, M. Choi, T. Sapsis, G. Karniadakis, A robust bi-orthogonal/dynamically-orthogonal method using the covariance pseudo-inverse with application to stochastic flow problemsJournal of Computational Physics344 (2017) 303-319. [pdf].
  • H. Babaee, T. Sapsis, A minimization principle for the description of time-dependent modes associated with transient instabilitiesProceedings of the Royal Society A472 (2016) 20150779 (27 pages). [pdf] Featured on the journal's cover page.
  • M. Choi, T. Sapsis, G. E. Karniadakis, On the equivalence of dynamically orthogonal and dynamically bi-orthogonal methods: Theory and numerical simulationsJournal of Computational Physics270 (2014) 1-20. [pdf]
  • T. Sapsis, A. Majda, Statistically accurate low order models for uncertainty quantification in turbulent dynamical systemsProceedings of the National Academy of Sciences110 (2013) 13705-13710.[pdf]
  • T. Sapsis, A. Majda, Blending modified Gaussian closure and non-Gaussian reduced subspace methods for turbulent dynamical systemsJournal of Nonlinear Science23 (2013) 1039 (33 pages). [pdf]
  • T. Sapsis, A. Majda, Blended reduced subspace algorithms for uncertainty quantification of quadratic systems with a stable mean statePhysica D258 (2013) 61-76. [pdf]
  • T. Sapsis, Attractor local dimensionality, nonlinear energy transfers, and finite-time instabilities in stochastic dynamical systems with applications to 2D fluid flowsProceedings of the Royal Society A469 (2013) 20120550 (23 pages). [pdf]
  • T. Sapsis, A. Majda, A statistically accurate modified quasilinar Gaussian closure for uncertainty quantification in turbulent dynamical systemsPhysica D252 (2013) 34-45. [pdf]
  • T. Sapsis, and H. A. Dijkstra, Interaction of additive noise and nonlinear dynamics in the double-gyre wind-driven ocean circulationJournal of Physical Oceanography43 (2013) 366-381. [pdf]
  • T. Sapsis, M. Ueckermann, P. Lermusiaux, Global analysis of Navier-Stokes and Boussinesq stochastic flows using dynamical orthogonalityJournal of Fluid Mechanics734 (2013) 83-113. [pdf]
  • M. Choi, T. Sapsis, G. E. Karniadakis, A convergence study for SPDEs using combined polynomial chaos and dynamically-orthogonal schemesJournal of Computational Physics245 (2013) 281-301. [pdf]
  • M. Ueckermann, P. Lermusiaux, T. Sapsis, Numerical schemes for dynamically orthogonal equations of stochastic fluid and ocean flowsJournal of Computational Physics233 (2013) 272-294. [pdf]
  • T. Sapsis & P. Lermusiaux, Dynamical criteria for the evolution of the stochastic dimensionality in flows with uncertaintyPhysica D241 (2012) 60-76. [pdf]
  • T. Sapsis & P. Lermusiaux, Dynamically orthogonal field equations for continuous stochastic dynamical systemsPhysica D238 (2009) 2347-2360. [pdf]
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Random Vibrations

  • V. Belenky, D. Glotzer, V. Pipiras, T. Sapsis, Distribution tail structure and extreme value analysis of constrained piecewise linear oscillatorsProbabilistic Engineering Mechanics57 (2019) 1-13. [pdf]
  • H. -K. JooM. Mohamad, T. Sapsis, Heavy-tailed response of structural systems subjected to extreme forcing eventsASME Journal of Computational and Nolinear Dynamics, 13 (2018) 090914 (12 pages). [pdf]
  • H. -K. JooM. Mohamad, T. Sapsis, Extreme events and their optimal mitigation in nonlinear structural systems excited by stochastic loads: Application to ocean engineering systemsOcean Engineering Journal142 (2017) 145-160. [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]
  • 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, A. Vakakis, & L. Bergman, Effect of stochasticity on targeted energy transfer from a linear medium to a strongly nonlinear attachmentProbabilistic Engineering Mechanics26 (2011) 119-133. [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]
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Bubbles and Particles in Fluid Flows

  • Z. Y. Wan, P. Karnakov, P. Koumoutsakos, T. Sapsis, Bubbles in turbulent flows: Data-driven, kinematic models with history termsInternational Journal of Multiphase Flows, 129 (2020) 103286 (11 pages)[pdf]
  • N. Aksamit, T. Sapsis, G. Haller, Machine-learning ocean dynamics from Lagrangian drifter trajectoriesJournal of Physical Oceanography, 50 (2020) 1179-1196[pdf]
  • S. Bryngelson, A. Charalampopoulos, T. Sapsis, T. Colonius, A Gaussian moment method and its augmentation via LSTM recurrent neural networks for the statistics of cavitating bubble populationsInternational Journal of Multiphase Flows, 127 (2020) 103262 (8 pages)[pdf]
  • M. Farazmand, T. Sapsis, Surface waves enhance particle dispersionFluids, 4 (2019) (12 pages). [pdf]
  • Z. Y. Wan, T. Sapsis, Machine learning the kinematics of spherical particles in fluid flowsJournal of Fluid Mechanics857 (2018) R2 (11 pages). [Code] [pdf]
  • T. Sapsis, N. Ouellette, J. Gollub, & G. Haller, Neutrally buoyant particle dynamics in fluid flows: Comparison of Experiments with Lagrangian stochastic modelsPhysics of Fluids23 (2011) 093304 (15 pages). [pdf]
  • T. Sapsis, J. Peng, & G. Haller, Instabilities on prey dynamics in jellyfish feedingBulletin of Mathematical Biology73 (2011) 1841-1856. [pdf]
  • T. Sapsis & G. Haller, Clustering criterion for inertial particles in 2D time-periodic and 3D steady flowsChaos20 (2010) 017515 (11 pages). [pdf]
  • G. Haller & T. Sapsis, Localized instability and attraction along invariant manifoldsSIAM Journal of Applied Dynamical Systems9 (2010) 611-633. [pdf]
  • T. Sapsis & G. Haller, Inertial particle dynamics in a hurricaneJournal of the Atmospheric Sciences66 (2009) 2481-2492. [pdf]
  • T. Sapsis & G. Haller, Instabilities in the dynamics of neutrally buoyant particlesPhysics of Fluids20 (2008) 017102 (7 pages). [pdf]
  • G. Haller, T. Sapsis, Where do inertial particles go in fluid flows?Physica D237 (2008) 573-583. [pdf]
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Statistics of Extremes

  • T. Sapsis, Statistics of extreme events in fluid flows and waves, Annual Review of Fluid Mechanics, 53 (2021) 85-111[pdf]
  • A. Blanchard, T. Sapsis, Output-weighted optimal sampling for Bayesian experimental design and rare-event quantificationSubmitted(2020)[pdf]
  • T. Sapsis, Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samplesProceedings of the Royal Society A, 476 (2020) 20190834[Code][pdf]
  • V. Belenky, D. Glotzer, V. Pipiras, T. Sapsis, Distribution tail structure and extreme value analysis of constrained piecewise linear oscillatorsProbabilistic Engineering Mechanics, 57 (2019) 1-13. [pdf]
  • M. Mohamad, T. Sapsis, A sequential sampling strategy for extreme event statistics in nonlinear dynamical systemsProceedings of the National Academy of Sciences115 (2018) 11138-11143. [open access link], [supporting information], Featured on the MIT News
  • T. Sapsis, New perspectives for the prediction and statistical quantification of extreme events in high-dimensional dynamical systemsPhilosophical Transactions of the Royal Society A376 (2018) 20170133 (18 pages). [pdf]
  • H. -K. JooM. Mohamad, T. Sapsis, Heavy-tailed response of structural systems subjected to extreme forcing eventsASME Journal of Computational and Nolinear Dynamics, 13 (2018) 090914 (12 pages). [pdf]
  • H. -K. JooM. Mohamad, T. Sapsis, Extreme events and their optimal mitigation in nonlinear structural systems excited by stochastic loads: Application to ocean engineering systemsOcean Engineering Journal142 (2017) 145-160. [pdf]
  • M. MohamadW. Cousins, T. Sapsis, A probabilistic decomposition-synthesis method for the quantification of rare events due to internal instabilitiesJournal of Computational Physics322 (2016) 288-308. [pdf]
  • M. Mohamad, T. Sapsis, Probabilistic response and rare events in Mathieu's equation under correlated parametric excitationOcean Engineering Journal, 120 (2016) 289-297. [pdf]
  • M. Mohamad, T. Sapsis, Probabilistic description of extreme events in intermittently unstable dynamical systems excited by correlated stochastic processesSIAM/ASA Journal on Uncertainty Quantification3 (2015) 709-736. [pdf]
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Prediction of Extremes

  • A. Blanchard, T. Sapsis, Bayesian optimization with output-weighted importance sampling, Submitted, (2020) (22 pages). [code][pdf]
  • S. Guth, T. Sapsis, Machine learning predictors of extreme events occurring in complex dynamical systems, Entropy, 21 (2019) 925 (18 pages). [pdf]
  • P. Blonigan, M. Farazmand, T. Sapsis, Are extreme dissipation events predictable in turbulent fluid flows?Physical Review Fluids, 4 (2019) 044606 (21 pages). [pdf]
  • W. Cousins, M. Onorato, A. Chabchoub, T. Sapsis, Predicting ocean rogue waves from point measurements: an experimental studyPhysical Review E, 99 (2019) 032201 (9 pages). [pdf]
  • M. Farazmand, T. Sapsis, Extreme events: mechanisms and prediction, ASME Applied Mechanics Reviews, 71 (2019) 050801. [pdf]
  • T. Sapsis, New perspectives for the prediction and statistical quantification of extreme events in high-dimensional dynamical systemsPhilosophical Transactions of the Royal Society A376 (2018) 20170133 (18 pages). [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]
  • M. Farazmand, T. Sapsis, Physics-based probing and prediction of extreme eventsSIAM News51 (2018) 1. [link] [pdf]
  • M. Farazmand, T. Sapsis, A variational approach to probing extreme events in turbulent dynamical systemsScience Advances3:e1701533 (2017) (7 pages). [pdf]
  • M. Farazmand, T. Sapsis, Reduced-order prediction of rogue waves in two dimensional water wavesJournal of Computational Physics340 (2017) 418-434. [pdf]
  • M. Farazmand, T. Sapsis, Dynamical indicators for the prediction of bursting phenomena in high-dimensional systemsPhysical Review E94 (2016) 032212 (15 pages). [pdf] Featured on the Physical Review E: Kaleidoscope.
  • W. Cousins, T. Sapsis, Reduced order precursors of rare events in unidirectional nonlinear water waves, Journal of Fluid Mechanics790 (2016) 368-388. [pdf] Featured as MIT spotlight. Reported by The Economist.
  • W. Cousins, T. Sapsis, The unsteady evolution of localized unidirectional deep water wave groupsPhysical Review E91 (2015) 063204 (5 pages). [pdf]
  • W. Cousins, T. Sapsis, Quantification and prediction of extreme events in a one-dimensional nonlinear dispersive wave modelPhysica D, 280-281 (2014) 48-58. [pdf]
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Control of Transient Instabilities

  • M. Farazmand, T. Sapsis, Closed-loop adaptive control of extreme events in a turbulent flowPhysical Review E, 100 (2019) 033110 (7 pages)[pdf]
  • A. Blanchard, T. Sapsis, Stabilization of Unsteady Flows by Reduced-Order Control with Optimally Time-Dependent ModesPhysical Review Fluids, 4 (2019) 053902 (27 pages). Editor's Suggestion[pdf]
  • A. Blanchard, S. Mowlavi, T. Sapsis, Control of linear instabilities by dynamically consistent order reduction on optimally time-dependent modes, Nonlinear Dynamics95 (2019) 2745-2764. [pdf]
  • H. -K. JooM. Mohamad, T. Sapsis, Extreme events and their optimal mitigation in nonlinear structural systems excited by stochastic loads: Application to ocean engineering systemsOcean Engineering Journal142 (2017) 145-160. [pdf]
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Energy Transfers in Mechanical Systems and Energy Harvesting

  • M. Haji, J. Kluger, T. Sapsis, A. Slocum, A symbiotic approach to the design of offshore wind turbines with other energy harvesting systemsOcean Engineering Journal169 (2018) 673-681. [pdf]
  • M. Haji, J. Kluger, J. Carrus, T. Sapsis, A. Slocum, Experimental investigation of hydrodynamic response of an ocean uranium extraction machine attached to a floating wind turbineInternational Journal of Offshore and Polar Engineering28 (2018) 225-231. [pdf]
  • A. Blanchard, T. Sapsis, A. Vakakis, Non-reciprocity in nonlinear elastodynamicsJournal of Sound and Vibration412 (2018) 326-335. [pdf]
  • O. Gendelman, T. Sapsis, Energy exchange and localization in essentially nonlinear oscillatory systems: Canonical formalismASME Journal of Applied Mechanics84 (2017) 011009 (9 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]
  • J. Kluger, T. Sapsis, A. Slocum, Robust energy harvesting from walking vibrations by means of nonlinear cantilever beamsJournal of Sound and Vibration341 (2015) 174-194. [pdf]
  • H. -K. Joo, T. Sapsis, Closure schemes for nonlinear bistable systems subjected to correlated Noise: Applications to energy harvesting from water wavesJournal of Ocean and Wind Energy2 (2015) 65-72. [pdf]
  • K. Remick, H.-K. Joo, D.M. McFarland, T. Sapsis, L. Bergman, D.D. Quinn, A. Vakakis, Sustained high-frequency energy harvesting through a strongly nonlinear electromechanical system under single and repeated impulsive excitationsJournal of Sound and Vibration333 (2014) 3214-3235. [pdf]
  • K. Remick, A. Vakakis, L. Bergman, D. M. McFarland, D. D. Quinn, T. Sapsis, Sustained high-frequency dynamic instability of a nonlinear system of coupled oscillators forced by single or repeated impulses: Theoretical and experimental resultsASME Journal of Vibration & Acoustics136 (2013) 011013 (15 pages). [pdf]
  • T. Sapsis, D. Quinn, A. Vakakis, & L. Bergman, Effective stiffening and damping enhancement of structures with strongly nonlinear local attachmentsASME Journal of Vibration & Acoustics134 (2012) 011016 (12 pages). [pdf]
  • T. Sapsis, A. Vakakis, & L. Bergman, Effect of stochasticity on targeted energy transfer from a linear medium to a strongly nonlinear attachmentProbabilistic Engineering Mechanics26 (2011) 119-133. [pdf]
  • O. Gendelman, T. Sapsis, A. Vakakis, L. Bergman, Enhanced passive targeted energy transfer in strongly nonlinear mechanical oscillatorsJournal of Sound and Vibration330 (2011) 1-8. [pdf]
  • T. Sapsis & A. Vakakis, Subharmonic orbits of a strongly nonlinear oscillator forced by closely spaced harmonicsJournal of Computational and Nonlinear Dynamics6 (2011) 011014 (10 pages). [pdf]
  • T. Sapsis, A. Vakakis, O. Gendelman, L. Bergman, G. Kerschen, & D. Quinn. Efficiency of targeted energy transfers in coupled nonlinear oscillators associated with 1:1 resonance captures: Part II, analytical studyJournal of Sound and Vibration325 (2009) 297-320. [pdf]
  • D. Quinn, O. Gendelman, G. Kerschen, T. Sapsis, L. Bergman, & A. Vakakis. Efficiency of targeted energy transfers in coupled nonlinear oscillators associated with 1:1 resonance aaptures: Part IJournal of Sound and Vibration311 (2008) 1228-1248. [pdf]