Journal Papers

Click here to see journal papers organized by areas OR search in the box below by topic (e.g. extreme events, turbulent systems, random vibrations, data driven methods, uncertainty, etc.), by author, or by year.

Supervised Students and Postdoctoral Scholars underlined



E. Pickering, T. Sapsis, Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier modelsSubmitted, (2022) (14 pages). [pdf]Machine learning, Bayesian analysis
J. Zhang, L. Cammarata, C. Squires, T. P. Sapsis, and C. Uhler, Active learning for optimal intervention design in causal modelsSubmitted, (2022) (44 pages). [pdf]Active learning, causality analysis
B. Champenois, T. Sapsis, Real-time reconstruction of 3D ocean temperature fields from reanalysis data and satellite and buoy surface measurementsSubmitted, (2022) (25 pages). [pdf]Data-driven modeling, ocean modeling, uncertainty quantification
E. Pickering, G. Karniadakis, T. Sapsis, Discovering and forecasting extreme events via active learning in neural operatorsSubmitted, (2022) (19 pages). [pdf]Extreme events, Data-driven modeling
S. Rudy, T. Sapsis, Output-weighted and relative entropy loss functions for deep learning precursors of extreme eventsSubmitted, (2022) (20 pages). [pdf]Extreme events, Data-driven modeling
C. Carvalho Da Silva, C. Lessig, B. Dodov, H. Dijkstra, T. Sapsis, A local spectral exterior calculus for the sphere and application to the rotating shallow water equationsSubmitted(2022) (42 pages). [pdf]Multiscale analysis, Wavelets, Fluid Flows
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, Accepted (2022) (34 pages). [pdf]Extreme events, Data-driven modeling, ship loads
A. Charalampopoulos, T. Sapsis, Uncertainty quantification of turbulent systems via physically consistent and data-informed reduced-order models, Physics of Fluids, 34 (2022) 075120 (16 pages). [pdf]Uncertainty quantification, Order reduction, Turbulent systems
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). [pdf]Active learning, Experimental design
T. Sapsis, A. BlanchardOptimal criteria and their asymptotic form for data selection in data-driven reduced-order modeling with Gaussian process regression, Philosophical Transactions of the Royal Society A, 380 (2022) 20210197 (12 pages). [pdf]Active learning, Experimental design, Order reduction
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 vibrationsJournal of Fluids and Structures, 112 (2022) 103607. [pdf]Vortex-induced Vibrations, Data-driven modeling
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] [MIT News]Nonlinear waves, Data-driven modeling
S. Rudy, T. Sapsis, Prediction of intermittent fluctuations from surface pressure measurements on a turbulent airfoil, American Institute of Aeronautics and Astronautics (AIAA) Jounal, 60 (2022) 4174-4190. [pdf]Turbulent flows, Extreme events, Order reduction, Data-driven modeling
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]Nonlinear vibrations
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 dynamicsPhilosophical Transactions of the Royal Society A, 380 (2022) 20210209 (19 pages). [pdf]Bubbles, Data-driven modeling
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]Extreme events, Uncertainty quantification
S. Atis, M. Leclair, T. Sapsis, T. Peacock, Anisotropic Particles Focusing Effect in Complex Flows, Physical Review Fluids, 7 (2022) 084503 (12 pages). [pdf]Finite size particles, Experimental work
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]Turbulent closures, Data-driven modeling
A. Blanchard, T. Sapsis, Informative path planning for anomaly detection in environment exploration and monitoring, Ocean Engineering, 43 (2022) 110242. [pdf]Experimental design, Optimization, Extreme events
A. Blanchard, G. C. Maceda, D. Fan, Y. Li, Y. Zhou, B. Noack, T. Sapsis, Bayesian optimization for active flow controlActa Mechanica Sinica, 37 (2021) 1786-1798. [pdf]Turbulent flows, Data-driven optimization, Control
S. Guth, T. Sapsis, Probabilistic characterization of the effect of transient stochastic loads on the fatigue-crack nucleation timeProbabilistic Engineering Mechanics, 66 (2021) 103162 (11 pages). [Code] [pdf]Fatigue, Extreme events, Uncertainty quantification
Z. Wan, B. Dodov, C. Lessig, H. Dijkstra, T. Sapsis, A data-driven framework for the stochastic reconstruction of small-scale features with applications to climate data setsJournal of Computational Physics, 442 (2021) 110484 (24 pages). [movie] [pdf]Uncertainty quantification, Climate modeling, Data-driven modeling
A. Blanchard, T. Sapsis, Output-weighted optimal sampling for Bayesian experimental design and rare-event quantificationSIAM/ASA Journal of Uncertainty Quantification, 9 (2021) 564-592. [Code] [pdf]Experimental design, Uncertainty quantification, extreme events
T. Sapsis, Statistics of extreme events in fluid flows and wavesAnnual Review of Fluid Mechanics,  53 (2021) 85-111. [free access pdf]Extreme events, Uncertainty quantification, Review
A. Blanchard, T. Sapsis, Bayesian optimization with output-weighted optimal samplingJournal of Computational Physics, 425 (2021) 109901 (16 pages). [Code] [pdf]Optimization
S. Rudy, T. Sapsis, Sparse methods for automatic relevance determinationPhysica D, 418 (2021) 132843 (16 pages). [Code] [pdf]Data-driven modeling
T. Sapsis, Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samplesProceedings of the Royal Society A, 476 (2020) 20190834 (24 pages). [Code] [pdf]Extreme events, Uncertainty quantification
Z. Y. Wan, P. Karnakov, P. Koumoutsakos, T. Sapsis, Bubbles in turbulent flows: Data-driven, kinematic models with history termsInt. Journal of Multiphase Flows, 129 (2020) 103286 (11 pages)[pdf]Finite size particles, Data driven modeling
S. Bryngelson, A. Charalampopoulos, T. Sapsis, T. ColoniusA Gaussian moment method and its augmentation via LSTM recurrent neural networks for the statistics of cavitating bubble populationsInt. Journal of Multiphase Flows, 127 (2020) 103262 (8 pages)[pdf]Finite size particles, Data driven modeling
Z. Vlachas, J. Pathak, B. R. Hunt, T. Sapsis, M. Girvan, E. Ott, P. KoumoutsakosForecasting of Spatio-temporal Chaotic Dynamics with Recurrent Neural Networks: a comparative study of Reservoir Computing and Backpropagation AlgorithmsNeural Networks, 126 (2020) 191-217[pdf]Data driven modeling, Chaotic systems
A. Athanassoulis, G. Athanassoulis, M. Ptashnyk, T. SapsisStrong solutions for the Alber equation and stability of unidirectional wave spectraKinetic and Related Models, 13 (2020) 703-737[pdf]Extreme events, Nonlinear waves
N. Aksamit, T. Sapsis, G. Haller, Machine-learning ocean dynamics from Lagrangian drifter trajectoriesJournal of Physical Oceanography, 50 (2020) 1179-1196[pdf]Finite size particles, Data driven modeling
A. Blanchard, T. Sapsis, Learning the tangent space of dynamical instabilities from dataChaos, 29 (2019) 113120, Focus Issue: When Machine Learning Meets Complex Systems: Networks, Chaos and Nonlinear Dynamics, (15 pages)[pdf]Order reduction, Data driven modeling
S. Guth, T. Sapsis, Machine learning predictors of extreme events occurring in complex dynamical systemsEntropy, 21 (2019) 925 (18 pages)[pdf]Extreme events, Data driven modeling
M. Farazmand, T. Sapsis, Closed-loop adaptive control of extreme events in a turbulent flowPhysical Review E, 100 (2019) 033110 (7 pages)[pdf]Control, Order reduction, Chaotic flows, Extreme events
A. Blanchard, T. Sapsis, Stabilization of unsteady flows by reduced-order control with optimally time-dependent modesPhysical Review Fluids4 (2019) 053902 (27 pages)[pdf], Editor's Suggestion. Control, Order reduction, Chaotic flows
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]Extreme events, Uncertainty quantification
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]Extreme events, Order reduction 
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]Extreme events, Nonlinear waves
P. Blonigan, M. Farazmand, T. Sapsis, Are extreme dissipation events predictable in turbulent fluid flows?Physical Review Fluids, 4 (2019) 044606 (21 pages)[pdf]Extreme events, Turbulent Systems
M. Farazmand, T. Sapsis, Surface waves enhance particle dispersionFluids, 4 (2019) (12 pages)[pdf]Water waves, Particles dispersion
A. Blanchard, S. Mowlavi, T. Sapsis, Control of linear instabilities by dynamically consistent order reduction on optimally time-dependent modesNonlinear Dynamics, 95 (2019) 2745-2764[pdf]Control, Order reduction, Chaotic flows
M. Mohamad, T. Sapsis, A sequential sampling strategy for extreme event statistics in nonlinear dynamical systemsProceedings of the National Academy of Sciences, 115 (2018) 11138-11143. [open access link], [supporting information]Featured on the MIT News. Extreme events, Uncertainty quantification
M. Farazmand, T. Sapsis, Extreme events: mechanisms and prediction, ASME Applied Mechanics Reviews, 71 (2019) 050801[pdf]Extreme events, Review
Z. Y. Wan, T. Sapsis, Machine learning the kinematics of spherical particles in fluid flowsJournal of Fluid Mechanics, 857 (2018) R2 (11 pages). [Code] [pdf]Finite size particles, Data driven modeling
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] Extreme events, Review
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] Data driven modeling, Extreme events, Turbulent systems
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 A, 474 (2018) 20170844 (20 pages)[pdf]Data driven modeling, Turbulent systems
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] Energy harvesting, Random vibrations
M. Haji, J. Kluger, J. Carrus, T. Sapsis, A. SlocumExperimental investigation of hydrodynamic response of an ocean uranium extraction machine attached to a floating wind turbineInternational Journal of Offshore and Polar Engineering, 28 (2018) 225-231. [pdf]Energy harvesting, Random vibrations
S. Mowlavi, T. Sapsis, Model order reduction for stochastic dynamical systems with continuous symmetriesSIAM Journal on Scientific Computing, 40 (2018) 1669-1695. [pdf]Uncertainty quantification, Order reduction
M. Farazmand, T. Sapsis, Physics-based probing and prediction of extreme eventsSIAM News, 51 (2018) 1[link] [pdf]Extreme events, Nonlinear waves, Turbulent systems
D. Baleanu, T. Kalmar-Nagy, T. Sapsis, & H. Yabano, Editorial for special issue on Nonlinear Dynamics: Models, Behavior, and Techniques, ASME Journal of Computational and Nolinear Dynamics, 13 (2018) 090301 (2 pages). [pdf]Editorial, Nonlinear vibrations, Random vibrations
H. -K. JooM. 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]Extreme events, Random vibrations
A. Blanchard, T. Sapsis, A. Vakakis, Non-reciprocity in nonlinear elastodynamicsJournal of Sound and Vibration, 412 (2018) 326-335. [pdf]Nonlinear waves
M. Farazmand, T. Sapsis, A variational approach to probing extreme events in turbulent dynamical systemsScience Advances3:e1701533 (2017) (7 pages). [pdf]Extreme events, Turbulent systems
A. Athanassoulis, G. Athanassoulis, T. Sapsis, Localized instabilities of the Wigner equation as a model for the emergence of rogue WavesJ. Ocean Eng. Mar. Energy3 (2017) 353-372[pdf]Extreme events, Nonlinear waves
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 Journal, 142 (2017) 145-160. [pdf]Extreme events, Random vibrations
H. BabaeeM. Farazmand, G. Haller, T. Sapsis, Reduced-order description of transient instabilities and computation of finite-time Lyapunov exponentsChaos, 27 (2017) 063103 (12 pages). [pdf]Extreme events, Order reduction
J.M. Kluger, A.H. Slocum, and T. Sapsis, Ring-based stiffening flexure applied as a load cell with high resolution and large force rangeASME Journal of Mechanical Design, 139 (2017) 103501 (8 pages). [pdf]Nonlinear load cells
M. Farazmand, T. Sapsis, Reduced-order prediction of rogue waves in two dimensional water wavesJournal of Computational Physics340 (2017) 418-434. [pdf]Extreme events, Nonlinear waves
Z. Y. Wan, T. Sapsis, Reduced-space Gaussian process regression for data-driven probabilistic forecast of chaotic dynamical systemsPhysica D, 345 (2017) 40-55. [pdf]Data driven modeling, Turbulent systems
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]Uncertainty quantification, Order reduction
O. Gendelman, T. Sapsis, Energy exchange and localization in essentially nonlinear oscillatory systems: Canonical formalismASME Journal of Applied Mechanics84 (2017) 011009 (9 pages). [pdf]Nonlinear vibrations
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.Extreme events, Order reduction, Turbulent systems
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]Extreme events, Nonlinear waves
M. Mohamad, T. Sapsis, Probabilistic response and rare events in Mathieu's equation under correlated parametric excitationOcean Engineering Journal120 (2016) 289-297. [pdf]Extreme events, Nonlinear vibrations
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.Extreme events, Order reduction, Turbulent systems
W. Cousins, T. Sapsis, Reduced order precursors of rare events in unidirectional nonlinear water wavesJournal of Fluid Mechanics, 790 (2016) 368-388. [pdf] Featured as MIT spotlight. Reported by The Economist.Extreme events, Nonlinear waves
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]Uncertainty quantification, Random vibrations
J. Kluger, T. Sapsis, A. Slocum, A high-resolution and large force-range load cell by means of nonlinear cantilever beamsPrecision Engineering43 (2016) 241-256. [pdf]Nonlinear load cells
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]Extreme events, Turbulent systems
W. Cousins, T. Sapsis, The unsteady evolution of localized unidirectional deep water wave groupsPhysical Review E91 (2015) 063204 (5 pages). [pdf]Extreme events, Nonlinear waves
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]Random vibrations, Energy harvesting
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]Random vibrations, Energy harvesting
A. Petsakou, T. Sapsis, J. Blau, Circadian rhythms in Rho1 activity regulate neuronal plasticity and network hierarchyCell162 (2015) 1-13. [pdf]Biology, Geometrical modeling
A. Majda, D. Qi, T. Sapsis, Blended particle filters for large dimensional chaotic dynamical systemsProceedings of the National Academy of Sciences111 (2014) 7511-7516. [pdf]Uncertainty quantification, Order reduction, Turbulent systems
W. Cousins, T. Sapsis, Quantification and prediction of extreme events in a one-dimensional nonlinear dispersive wave model, Physica D280-281 (2014) 48-58. [pdf]Extreme events, Nonlinear waves
H.-K. Joo, T. Sapsis, Performance measures for single-degree-of-freedom energy harvesters under stochastic excitationJournal of Sound and Vibration313 (2014) 4695-4710. [pdf]Nonlinear vibrations, Energy harvesting
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]Uncertainty quantification, Order reduction
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]Nonlinear vibrations, Energy harvesting
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]Uncertainty quantification, Order reduction, Turbulent systems
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 & Acoustics136 (2013) 011013 (15 pages). [pdf]Nonlinear vibrations, Energy harvesting
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]Uncertainty quantification, Order reduction, Turbulent systems
T. Sapsis, A. Majda, Blended reduced subspace algorithms for uncertainty quantification of quadratic systems with a stable mean statePhysica D258 (2013) 61-76. [pdf]Uncertainty quantification, Order reduction
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]Uncertainty Quantification, Order reduction, Chaotic flows
T. Sapsis, A. Majda, A statistically accurate modified quasilinar Gaussian closure for uncertainty quantification in turbulent dynamical systemsPhysica D252 (2013) 34-45. [pdf]Uncertainty quantification, Order reduction, Turbulent systems
T. Sapsis, and H. A. Dijkstra, Interaction of additive noise and nonlinear dynamics in the double-gyre wind-driven ocean circulation, Journal of Physical Oceanography43 (2013) 366-381. [pdf]Uncertainty quantification, Order reduction, Chaotic flows
T. Sapsis, M. Ueckermann, P. Lermusiaux, Global analysis of Navier-Stokes and Boussinesq stochastic flows using dynamical orthogonality, Journal of Fluid Mechanics734 (2013) 83-113. [pdf]Uncertainty quantification, Order reduction, Chaotic flows
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]Uncertainty quantification, Order reduction
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]Uncertainty quantification, Order reduction, Chaotic flows
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]Uncertainty quantification, Correlated excitation
T. Sapsis & P. Lermusiaux, Dynamical criteria for the evolution of the stochastic dimensionality in flows with uncertainty, Physica D241 (2012) 60-76. [pdf]Uncertainty quantification, Order reduction, Chaotic flows
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]Nonlinear vibrations
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]Finite size particles
G. Haller & T. Sapsis, Lagrangian coherent structures and the smallest finite-time Lyapunov exponentChaos21 (2011) 023115 (7 pages). [pdf]Lagrangian coherent structures
T. Sapsis, J. Peng, & G. Haller, Instabilities on prey dynamics in jellyfish feedingBulletin of Mathematical Biology73 (2011) 1841-1856. [pdf]Finite size particles
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]Nonlinear Vibrations, Random vibrations
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]Nonlinear vibrations
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]Nonlinear vibrations
T. Sapsis & G. Haller, Clustering criterion for inertial particles in 2D time-periodic and 3D steady flows, Chaos20 (2010) 017515 (11 pages). [pdf]Finite size particles
G. Haller & T. Sapsis, Localized instability and attraction along invariant manifoldsSIAM Journal of Applied Dynamical Systems9 (2010) 611-633. [pdf]Finite size particles
T. Sapsis & P. Lermusiaux, Dynamically orthogonal field equations for continuous stochastic dynamical systemsPhysica D238 (2009) 2347-2360. [pdf]Uncertainty quantification, Order reduction, Chaotic fluid flows
T. Sapsis & G. Haller, Inertial particle dynamics in a hurricane, Journal of the Atmospheric Sciences66 (2009) 2481-2492. [pdf]Finite size particles
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 Vibration325 (2009) 297-320. [pdf]Nonlinear vibrations
T. Sapsis & G. Haller, Instabilities in the dynamics of neutrally buoyant particles, Physics of Fluids20(2008) 017102 (7 pages). [pdf]Finite size particles
G. Haller, T. Sapsis, Where do inertial particles go in fluid flows?, Physica D237 (2008) 573-583. [pdf]Finite size particles
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]Uncertainty quantification, Correlated excitation
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]Nonlinear vibrations