Bayesian Analysis and Optimal Experimental Design
Dynamically Orthogonal Equations for Uncertainty Quantification of Dynamical Systems
Stochastic Closures and Data-Driven Modeling for Dynamical Systems
Optimal Time Dependent Modes for Reduced Order Modeling & Control of Transient Instabilities
Ship and Submarine Dynamics and Loads
Computational Methods for Climate Modeling
Bubbles and Particles in Fluid Flows
Nonlinear Mechanical Oscillations
Bayesian Analysis and Optimal Experimental Design
Theory-Algorithms
E. Pickering, T. Sapsis, Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models, Submitted, (2023) (14 pages). [pdf]
J. Zhang, L. Cammarata, C. Squires, T. P. Sapsis, and C. Uhler, Active learning for optimal intervention design in causal models, Nature Machine Intelligence, (2023) [pdf] Featured on the MIT News.
E. Pickering, S. Guth, G. Karniadakis, T. Sapsis, Discovering and forecasting extreme events via active learning in neural operators, Nature Computational Science, 2 (2022) 833-843. [pdf]
T. Sapsis, A. Blanchard, Optimal criteria and their asymptotic form for data selection in data-driven reduced-order modeling with Gaussian process regression, Philosophical Transcations of the Royal Society A, 380 (2022) 20210197 (12 pages). [pdf]
A. Blanchard, T. Sapsis, Output-weighted optimal sampling for Bayesian experimental design and rare-event quantification, SIAM/ASA Journal of Uncertainty Quantification, 9 (2021) 564-592. [code] [pdf]
S. Rudy, T. Sapsis, Sparse methods for automatic relevance determination, Physica D, 418 (2021) 132843 (16 pages). [code] [pdf]
A. Blanchard, T. Sapsis, Bayesian optimization with output-weighted optimal sampling, Journal of Computational Physics, 425 (2021) 109901 (16 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]
Applications
S. Guth, E. Katsidoniotaki, T. Sapsis, Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine foundations using wave episodes and targeted CFD simulations through active sampling (2023), Wind Energy, In Press (36 pages). [pdf].
B. Hammond, T. Sapsis, Reduced order modeling of hydrodynamic interactions between a submarine and unmanned underwater vehicle using non-myopic multi-fidelity active learning, Journal of Ocean Engineering, 288 (2023) 116016. [pdf]
Y. Yang, A. Blanchard, T. Sapsis, P. Perdikaris, Output-weighted sampling for multi-armed bandits with extreme payoffs, Proceedings of the Royal Society A, 478 (2022) 20210781 (17 pages). [code] [pdf]
A. Blanchard, G. C. Maceda, D. Fan, Y. Li, Y. Zhou, B. Noack, T. Sapsis, Bayesian optimization for active flow control, Acta Mechanica Sinica, 37 (2022) 1786-1798. [pdf]
A. Blanchard, T. Sapsis, Informative path planning for anomaly detection in environment exploration and monitoring, Ocean Engineering, (2021) 43 110242 (10 pages). [pdf]
Prediction of Extremes
Expositories-Reviews
M. Farazmand, T. Sapsis, Extreme events: mechanisms and prediction, ASME Applied Mechanics Reviews, 71(2019) 050801. Received the Lloyd Hamilton Donnell Best Paper Award for 2020. [pdf]
T. Sapsis, New perspectives for the prediction and statistical quantification of extreme events in high-dimensional dynamical systems, Philosophical Transactions of the Royal Society A, 376 (2018) 20170133 (18 pages). [pdf]
M. Farazmand, T. Sapsis, Physics-based probing and prediction of extreme events, SIAM News, 51 (2018) 1. [link] [pdf]
Theory-Algorithms
E. Pickering, S. Guth, G. Karniadakis, T. Sapsis, Discovering and forecasting extreme events via active learning in neural operators, Nature Computational Science, 2 (2022) 833-843. [pdf]
S. Rudy, T. Sapsis, Output-weighted and relative entropy loss functions for deep learning precursors of extreme events, Physica D,443 (2023) 133570 (12 pages). [pdf]
A. Blanchard, T. Sapsis, Bayesian optimization with output-weighted importance sampling, Journal of Computational Physics, 425 (2021) 109901 (16 pages). [code][pdf]
S. Guth, T. Sapsis, Machine learning predictors of extreme events occurring in complex dynamical systems, Entropy, 21 (2019) 925 (18 pages). [Code][pdf]
M. Farazmand, T. Sapsis, A variational approach to probing extreme events in turbulent dynamical systems, Science Advances, 3:e1701533 (2017) (7 pages). [pdf]
W. Cousins, T. Sapsis, Reduced order precursors of rare events in unidirectional nonlinear water waves, Journal of Fluid Mechanics, 790 (2016) 368-388. [pdf] Featured as MIT spotlight. Reported by The Economist.
W. Cousins, T. Sapsis, Quantification and prediction of extreme events in a one-dimensional nonlinear dispersive wave model, Physica D, 280-281 (2014) 48-58. [pdf]
Applications
B. Barthel, T. Sapsis, Harnessing the instability mechanisms in airfoil flow for the data-driven forecasting of extreme events, American Institute of Aeronautics and Astronautics (AIAA) Journal, 61 (2023). [pdf]
S. Rudy, T. Sapsis, Prediction of intermittent fluctuations from surface pressure measurements on a turbulent airfoil, American Institute of Aeronautics and Astronautics (AIAA) Journal, 60 (2022) 4174-4190. [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]
M. Farazmand, T. Sapsis, Closed-loop adaptive control of extreme events in a turbulent flow, Physical Review E, 100 (2019) 033110 (7 pages). [pdf]
W. Cousins, M. Onorato, A. Chabchoub, T. Sapsis, Predicting ocean rogue waves from point measurements: an experimental study, Physical Review E, 99 (2019) 032201 (9 pages). [pdf]
Z. Y. Wan, P. Vlachas, P. Koumoutsakos, T. Sapsis, Data-assisted reduced-order modeling of extreme events in complex dynamical systems, PLOS One, 24 May (2018) (22 pages). [pdf]
M. Farazmand, T. Sapsis, Reduced-order prediction of rogue waves in two dimensional water waves, Journal of Computational Physics, 340 (2017) 418-434. [pdf]
M. Farazmand, T. Sapsis, Dynamical indicators for the prediction of bursting phenomena in high-dimensional systems, Physical Review E, 94 (2016) 032212 (15 pages). [pdf] Featured on the Physical Review E: Kaleidoscope.
W. Cousins, T. Sapsis, The unsteady evolution of localized unidirectional deep water wave groups, Physical Review E, 91 (2015) 063204 (5 pages). [pdf]
Order Reduction for Uncertainty Quantification of Dynamical Systems
Theory-Algorithms
S. Mowlavi, T. Sapsis, Model order reduction for stochastic dynamical systems with continuous symmetries, SIAM Journal on Scientific Computing, 40 (2018) 1669-1695. [pdf]
T. Sapsis, A. Majda, Statistically accurate low order models for uncertainty quantification in turbulent dynamical systems, Proceedings of the National Academy of Sciences, 110 (2013) 13705-13710.[pdf]
T. Sapsis, A. Majda, Blending modified Gaussian closure and non-Gaussian reduced subspace methods for turbulent dynamical systems, Journal of Nonlinear Science, 23 (2013) 1039 (33 pages). [pdf]
T. Sapsis, A. Majda, Blended reduced subspace algorithms for uncertainty quantification of quadratic systems with a stable mean state, Physica D, 258 (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 flows, Proceedings of the Royal Society A, 469 (2013) 20120550 (23 pages). [pdf]
T. Sapsis & P. Lermusiaux, Dynamical criteria for the evolution of the stochastic dimensionality in flows with uncertainty, Physica D, 241 (2012) 60-76. [pdf]
T. Sapsis & P. Lermusiaux, Dynamically orthogonal field equations for continuous stochastic dynamical systems, Physica D, 238 (2009) 2347-2360. [pdf]
Applications
S. Guth, A. Mojahed, T. Sapsis, Quality measures for the evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems, Computer Methods in Applied Mechanics and Engineering, (2024) In Press (27 pages) [pdf].
B. Champenois, T. Sapsis, Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements, Physica D, 459 (2024) 134026. [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 problems, Journal of Computational Physics, 344 (2017) 303-319. [pdf].
M. Choi, T. Sapsis, G. E. Karniadakis, On the equivalence of dynamically orthogonal and dynamically bi-orthogonal methods: Theory and numerical simulations, Journal of Computational Physics, 270 (2014) 1-20. [pdf]
M. Ueckermann, P. Lermusiaux, T. Sapsis, Numerical schemes for dynamically orthogonal equations of stochastic fluid and ocean flows, Journal of Computational Physics, 233 (2013) 272-294. [pdf]
T. Sapsis, and H. A. Dijkstra, Interaction of additive noise and nonlinear dynamics in the double-gyre wind-driven ocean circulation, Journal of Physical Oceanography, 43 (2013) 366-381. [pdf]
T. Sapsis, M. Ueckermann, P. Lermusiaux, Global analysis of Navier-Stokes and Boussinesq stochastic flows using dynamical orthogonality, Journal of Fluid Mechanics, 734 (2013) 83-113. [pdf]
M. Choi, T. Sapsis, G. E. Karniadakis, A convergence study for SPDEs using combined polynomial chaos and dynamically-orthogonal schemes, Journal of Computational Physics, 245 (2013) 281-301. [pdf]
Computational methods for climate modeLs
B. Champenois, T. Sapsis, Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements, Physica D, 459 (2024) 134026 (14 pages) . [pdf].
A. Charalampopoulos, S. Zhang, B. Harrop, L. R. Leung, and T. Sapsis, Statistics of extreme events in coarse-scale climate simulations via machine learning correction operators trained on nudged datasets, AAAI Conference: AI Climate Tipping-Point Discovery (3-2023). [pdf]
A. Blanchard, N. Parashar, B. Dodov, C. Lessig, T. Sapsis, A multi-scale deep learning framework for projecting weather extremes, Tackling Climate Change with Machine Learning workshop at NeurIPS 2022, New Orleans (12-2022). Best Paper: ML Innovation [pdf] [link]
Z. Wan, B. Dodov, C. Lessig, H. Dijkstra, T. Sapsis, A data-driven framework for the stochastic reconstruction of small-scale features with application to climate data sets, Journal of Computational Physics, 442 (2021) 110484 (24 pages). [movie] [pdf]
Nonlinear Mechanical Oscillations
Nonlinear Energy Transfers in Mechanical Systems
J. E. Chen, T. Theurich, M. Krack, T. Sapsis, L. A. Bergman, A. F. Vakakis, Intense cross-scale energy cascades resembling “mechanical turbulence” in harmonically driven strongly nonlinear hierarchical chains of oscillators, Acta Mechanica, 233 (2022) 1289-1305. [pdf]
A. Blanchard, T. Sapsis, A. Vakakis, Non-reciprocity in nonlinear elastodynamics, Journal of Sound and Vibration, 412 (2018) 326-335. [pdf]
O. Gendelman, T. Sapsis, Energy exchange and localization in essentially nonlinear oscillatory systems: Canonical formalism, ASME Journal of Applied Mechanics, 84 (2017) 011009 (9 pages). [pdf]
T. Sapsis, D. Quinn, A. Vakakis, & L. Bergman, Effective stiffening and damping enhancement of structures with strongly nonlinear local attachments, ASME Journal of Vibration & Acoustics, 134 (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 attachment, Probabilistic Engineering Mechanics, 26 (2011) 119-133. [pdf]
O. Gendelman, T. Sapsis, A. Vakakis, L. Bergman, Enhanced passive targeted energy transfer in strongly nonlinear mechanical oscillators, Journal of Sound and Vibration, 330 (2011) 1-8. [pdf]
T. Sapsis & A. Vakakis, Subharmonic orbits of a strongly nonlinear oscillator forced by closely spaced harmonics, Journal of Computational and Nonlinear Dynamics, 6 (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 study, Journal of Sound and Vibration, 325 (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 I, Journal of Sound and Vibration, 311 (2008) 1228-1248. [pdf]
Energy Harvesting
M. Haji, J. Kluger, T. Sapsis, A. Slocum, A symbiotic approach to the design of offshore wind turbines with other energy harvesting systems, Ocean Engineering Journal, 169 (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 turbine, International Journal of Offshore and Polar Engineering, 28 (2018) 225-231. [pdf]
H. -K. Joo, T. Sapsis, A moment-equation-copula-closure method for nonlinear vibrational systems subjected to correlated noise, Probabilistic Engineering Mechanics, 46 (2016) 120-132. [pdf]
J. Kluger, T. Sapsis, A. Slocum, Robust energy harvesting from walking vibrations by means of nonlinear cantilever beams, Journal of Sound and Vibration, 341 (2015) 174-194. [pdf]
H.-K. Joo, T. Sapsis, Performance measures for single-degree-of-freedom energy harvesters under stochastic excitation, Journal of Sound and Vibration, 313 (2014) 4695-4710. [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 excitations, Journal of Sound and Vibration, 333 (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 results, ASME Journal of Vibration & Acoustics, 136 (2013) 011013 (15 pages). [pdf]
Statistics of Extremes
Expositories-Reviews
T. Sapsis, Statistics of extreme events in fluid flows and waves, Annual Review of Fluid Mechanics, 53 (2021) 85-111. [free access pdf]
T. Sapsis, New perspectives for the prediction and statistical quantification of extreme events in high-dimensional dynamical systems, Philosophical Transactions of the Royal Society A, 376 (2018) 20170133 (18 pages). [pdf]
Theory-Algoritms
H. Arbabi, T. Sapsis, Generative stochastic modeling of strongly nonlinear flows with non-Gaussian statistics, SIAM/ASA Journal of Uncertainty Quantification, 10 (2022) 555-583. [code][pdf]
Z. Wan, B. Dodov, C. Lessig, H. Dijkstra, T. Sapsis, A data-driven framework for the stochastic reconstruction of small-scale features with application to climate data sets, Journal of Computational Physics, 442 (2021) 110484 (24 pages). [movie] [pdf]
A. Blanchard, T. Sapsis, Output-weighted optimal sampling for Bayesian experimental design and rare-event quantification, SIAM/ASA Journal of Uncertainty Quantification, 9 (2021) 564-592. [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. [Code][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], Featured on the MIT News.
M. Mohamad, W. Cousins, T. Sapsis, A probabilistic decomposition-synthesis method for the quantification of rare events due to internal instabilities, Journal of Computational Physics, 322 (2016) 288-308. [pdf]
M. Mohamad, T. Sapsis, Probabilistic description of extreme events in intermittently unstable dynamical systems excited by correlated stochastic processes, SIAM/ASA Journal on Uncertainty Quantification, 3 (2015) 709-736. [pdf]
Applications
S. Guth, E. Katsidoniotaki, T. Sapsis, Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine foundations using wave episodes and targeted CFD simulations through active sampling (2023), Wind Energy, In Press (36 pages). [pdf].
S. Guth, T. Sapsis, Wave episode based Gaussian process regression for extreme event statistics in ship dynamics: Between the Scylla of Karhunen-Loève convergence and the Charybdis of transient features, Ocean Engineering Journal, 266 (2022) 112633 (18 pages). [pdf]
S. Guth, T. Sapsis, Probabilistic characterization of the effect of transient stochastic loads on the fatigue-crack nucleation time, Probabilistic Engineering Mechanics, 66 (2021) 103162 (11 pages). [Code][pdf]
V. Belenky, D. Glotzer, V. Pipiras, T. Sapsis, Distribution tail structure and extreme value analysis of constrained piecewise linear oscillators, Probabilistic Engineering Mechanics, 57 (2019) 1-13.[pdf]
H. -K. Joo, M. Mohamad, T. Sapsis, Heavy-tailed response of structural systems subjected to extreme forcing events, ASME Journal of Computational and Nonlinear Dynamics, 13 (2018) 090914 (12 pages). [pdf]
H. -K. Joo, M. Mohamad, T. Sapsis, Extreme events and their optimal mitigation in nonlinear structural systems excited by stochastic loads: Application to ocean engineering systems, Ocean Engineering Journal, 142 (2017) 145-160. [pdf]
M. Mohamad, T. Sapsis, Probabilistic response and rare events in Mathieu’s equation under correlated parametric excitation, Ocean Engineering Journal,120 (2016) 289-297. [pdf]
Reduced Order Modeling & Control of Transient Instabilities
Theory-Algorithms
A. Blanchard, T. Sapsis, Learning the tangent space of dynamical instabilities from data, Chaos, 29 (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 instabilities, SIAM Journal on Applied Dynamical Systems, 18 (2019) 1143-1162. [pdf]
H. Babaee, M. Farazmand, G. Haller, T. Sapsis, Reduced-order description of transient instabilities and computation of finite-time Lyapunov exponents, Chaos, 27 (2017) 063103 (12 pages). [pdf]
H. Babaee, T. Sapsis, A minimization principle for the description of time-dependent modes associated with transient instabilities, Proceedings of the Royal Society A, 472 (2016) 20150779 (27 pages). [pdf] Featured on the journal’s cover page.
Applications
A. Blanchard, T. Sapsis, Stabilization of unsteady flows by reduced-order control with optimally time-dependent modes, Physical 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 Dynamics, 95 (2019) 2745-2764. [pdf]
Stochastic Closures and Data-Driven Modeling for Dynamical Systems
Stochastic closures for uncertainty quantification
A. Charalampopoulos, T. Sapsis, Uncertainty quantification of turbulent systems via physically consistent and data-informed reduced-order models, Physics of Fluids, 34 (2022) 075120. [pdf]
A. Charalampopoulos, T. Sapsis, Machine-learning energy-preserving nonlocal closures for turbulent fluid flows and inertial tracers, Physical Review Fluids, 7 (2022) 024305 (24 pages). [pdf]
H. -K. Joo, T. Sapsis, A moment-equation-copula-closure method for nonlinear vibrational systems subjected to correlated noise, Probabilistic Engineering Mechanics, 46 (2016) 120-132. [pdf]
T. Sapsis, A. Majda, A statistically accurate modified quasilinar Gaussian closure for uncertainty quantification in turbulent dynamical systems, Physica D, 252 (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 systems, Proceedings of the Royal Society A, 468 (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 excitation, Probabilistic Engineering Mechanics, 23 (2008) 289-306. [pdf]
Data-driven modeling for prediction
A. Mentzelopoulos, J. Ferrandis, S. Rudy, T. Sapsis, M. Triantafyllou, D. Fan, Data-driven prediction and study of vortex induced vibrations by leveraging hydrodynamic coefficient databases learned from sparse sensors, Ocean Engineering Journal, 266 (2022) 112833 (12 pages). [pdf]
D. Eeltink, H. Branger, C. Luneau, Y. He, A. Chabchoub, J. Kasparian, T. S. van den Bremer, T. Sapsis, Nonlinear wave evolution with data-driven breaking, Nature Communications 13 (2022) 2343. [pdf]
S. Rudy, T. Sapsis, Prediction of intermittent fluctuations from surface pressure measurements on a turbulent airfoil, American Institute of Aeronautics and Astronautics (AIAA) Journal, 60 (2022) 4174-4190.[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 vibrations, Journal of Fluids and Structures, 112 (2022) 103607.[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 algorithms, Neural 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 systems, PLOS 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 networks, Proceedings of the Royal Society A, 474 (2018) 20170844 (20 pages). [pdf]
Z. Y. Wan, T. Sapsis, Reduced-space Gaussian process regression for data-driven probabilistic forecast of chaotic dynamical systems, Physica D, 345 (2017) 40-55. [pdf]
Bubbles and Particles in Fluid Flows
Theory-Algorithms
M. Farazmand, T. Sapsis, Surface waves enhance particle dispersion, Fluids, 4 (2019) (12 pages). [pdf]
Z. Y. Wan, T. Sapsis, Machine learning the kinematics of spherical particles in fluid flows, Journal of Fluid Mechanics, 857 (2018) R2 (11 pages). [Code][pdf]
T. Sapsis & G. Haller, Clustering criterion for inertial particles in 2D time-periodic and 3D steady flows, Chaos, 20 (2010) 017515 (11 pages). [pdf]
G. Haller & T. Sapsis, Localized instability and attraction along invariant manifolds, SIAM Journal of Applied Dynamical Systems, 9 (2010) 611-633. [pdf]
T. Sapsis & G. Haller, Instabilities in the dynamics of neutrally buoyant particles, Physics of Fluids, 20 (2008) 017102 (7 pages). [pdf]
G. Haller, T. Sapsis, Where do inertial particles go in fluid flows?, Physica D, 237 (2008) 573-583. [pdf]
Applications
A. Charalampopoulos, S. H. Bryngelson, T. Colonius, T. Sapsis, Hybrid quadrature moment method for accurate and stable representation of non-Gaussian processes applied to bubble dynamics, Phil. Trans. Royal Soc. A, 380 (2022) 20210209 (19 pages). [pdf]
S. Atis, M. Leclair, T. Sapsis, T. Peacock, Anisotropic particles focusing effect in complex flows, Physical Review Fluids, 7 (2022) 084503 (12 pages). [pdf]
Z. Y. Wan, P. Karnakov, P. Koumoutsakos, T. Sapsis, Bubbles in turbulent flows: Data-driven, kinematic models with history terms, International Journal of Multiphase Flows, 129 (2020) 103286 (11 pages). [pdf]
N. Aksamit, T. Sapsis, G. Haller, Machine-learning ocean dynamics from Lagrangian drifter trajectories, Journal 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 populations, International Journal of Multiphase Flows, 127 (2020) 103262 (8 pages). [pdf]
T. Sapsis, N. Ouellette, J. Gollub, & G. Haller, Neutrally buoyant particle dynamics in fluid flows: Comparison of Experiments with Lagrangian stochastic models, Physics of Fluids, 23 (2011) 093304 (15 pages). [pdf]
T. Sapsis, J. Peng, & G. Haller, Instabilities on prey dynamics in jellyfish feeding, Bulletin of Mathematical Biology, 73 (2011) 1841-1856. [pdf]
T. Sapsis & G. Haller, Inertial particle dynamics in a hurricane, Journal of the Atmospheric Sciences, 66 (2009) 2481-2492. [pdf]
Ship and Submarine Dynamics and Loads
B. Hammond, T. Sapsis, UUV autonomy and control near submarines using actively sampled surrogates, Journal of Ship Research, 67 (2023), 235-251 [pdf]
D. Glotzer, V. Pipiras, V. Belenky, K. Weems, T. Sapsis, Distributions and extreme value analysis of critical response rate and split-time metric in nonlinear oscillators with stochastic excitation, Ocean Engineering Journal, 292 (2024) 116538 (12 pages). [pdf]
M. Levine, S. Edwards, D. Howard, K. Weems, T. Sapsis, V. Pipiras, Multi-fidelity data-adaptive autonomous seakeeping, Ocean Engineering Journal, 292 (2024) 116322 (13 pages). [pdf]
V. Belenky, K. Weems, W.-M. Lin, V. Pipiras, T. Sapsis, Estimation of probability of capsizing with split-time method, Ocean Engineering Journal, 292 (2024) 116452 (27 pages). [pdf]
B. Hammond, T. Sapsis, Reduced order modeling of hydrodynamic interactions between a submarine and unmanned underwater vehicle using non-myopic multi-fidelity active learning, Ocean Engineering Journal, 288 (2023) 116016. [pdf]
B. Campbell, V. Belenky, V. Pipiras, K. Weems, T. Sapsis, Estimation of probability of large roll angle with envelope peaks over threshold method, Ocean Engineering Journal, 290 (2023), 116296 (16 pages). [pdf]
S. Guth, T. Sapsis, Wave episode based Gaussian process regression for extreme event statistics in ship dynamics: Between the Scylla of Karhunen-Loève convergence and the Charybdis of transient features, Ocean Engineering Journal, 266 (2022) 112633 (18 pages). [pdf]
A. Kriezis, C. Chryssostomidis, T. Sapsis, Predicting ship power using machine learning methods, Proceedings of the SNAME Maritime Convention 2022, Houston, Texas, (9 – 2022). [pdf]
M. D. Levine, S. J. Edwards, D. Howard, V. Belenky, K. Weems, T. Sapsis, and V. Pipiras, Data-adaptive autonomous seakeeping, Proceedings of the 34th Symposium on Naval Hydrodynamics Washington, DC, USA (6 – 2022). [pdf]
S. Guth, B. Champenois, T. Sapsis, Application of Gaussian process multi-fidelity optimal sampling to ship structural modeling, Proceedings of the 34th Symposium on Naval Hydrodynamics Washington, DC, USA (6 – 2022). [pdf]
T. Sapsis, V. Belenky, K. Weems, V. Pipiras, Deck effects on the statistical structure of the vertical bending moment loads during random waves: an analytical approach, Proceedings of the 34th Symposium on Naval Hydrodynamics Washington, DC, USA (6 – 2022). [pdf]
V. Pipiras, D. Howard, V. Belenky, K. Weems and T. Sapsis, Multi-Fidelity Uncertainty Quantification and Reduced-Order Modeling for Extreme Ship Motions and Loads, Proceedings of the 34th Symposium on Naval Hydrodynamics Washington, DC, USA (6 – 2022). [pdf]
V. Belenky, K. Weems, T. Sapsis, V. Pipiras, Influence of deck submergence events on extreme properties of wave-induced vertical bending moment, Proceedings of the 1st International Conference on the Stability and Safety of Ships and Ocean Vehicles, Glasgow, Scotland, UK (Virtual), (6-2021). [pdf]
S. Guth, T. Sapsis, A stochastically preluded Karhunen-Loève representation for recovering extreme statistics in ship dynamics, Proceedings of the 1st International Conference on the Stability and Safety of Ships and Ocean Vehicles, Glasgow, Scotland, UK (Virtual), (6-2021). [pdf]
T. Sapsis, V. Belenky, K. Weems, V. Pipiras, Extreme properties of impact-induced vertical bending moments, Proceedings of the 1st International Conference on the Stability and Safety of Ships and Ocean Vehicles, Glasgow, Scotland, UK (Virtual), (6-2021). [pdf]
T. Sapsis, V. Pipiras, K. Weems, V. Belenky, On extreme value properties of vertical bending moment, Proceedings of the 33rd Symposium on Naval Hydrodynamics Osaka, Japan (Virtual) (10-2020). [pdf]
K. Weems, V. Belenky, B. Campbell, V. Pipiras, T. Sapsis, Envelope peaks over threshold application and verification, Proceedings of the 17th International Ship Stability Workshop, Helsinki, Finland (6-2019). [pdf]
V. Belenky, K. Weems, V. Pipiras, D. Glotzer, T. Sapsis, Tail structure of roll and metric of capsizing in irregular waves, Proceedings of the 32nd Symposium on Naval Hydrodynamics (2018), Hamburg, Germany (8-2018). [pdf]
V. Belenky, K. Weems, K. Spyrou, V. Pipiras, T. Sapsis, Modeling Broaching-to and Capsizing with Extreme Value Theory, Proceedings of the 16th International Ship Stability Workshop, Belgrade, Serbia (6-2017).
V. Belenky, D. Glozter, V. Pipiras, T. P. Sapsis, On the tail of nonlinear roll motions, Proceedings of the 15th International Ship Stability Workshop, 13-15 June 2016, Stockholm, Sweden (6-2016). [pdf]