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
| Paper | Topic |
96. | A. Blanchard, G. C. Maceda, D. Fan, Y. Li, Y. Zhou, B. Noack, T. Sapsis, Bayesian optimization for active flow control, Submitted, (2021) (13 pages). [pdf] | Turbulent flows, Data-driven optimization, Control |
95. | 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, Submitted, (2021) (46 pages). [pdf] | Vortex-induced Vibrations, Data-driven modeling |
94. | A. Blanchard, T. Sapsis, Informative path planning for anomaly detection in environment exploration and monitoring, Submitted, (2021) (24 pages). [pdf] | Experimental design, Optimization, Extreme events |
93. | F. Tsitoura, T. Sapsis, Probabilistic analysis of the dynamical mechanisms responsible for rare events in 2D waves, Submitted, (2021) (7 pages). [pdf] | Extreme events, Nonlinear waves |
92. | A. Charalampopoulos, T. Sapsis, Machine-learning energy-preserving nonlocal closures for turbulent fluid flows and inertial tracers, Submitted, (2021) (25 pages). [pdf] | Turbulent closures, Data-driven modeling |
91. | S. Guth, T. Sapsis, Probabilistic characterization of the effect of transient stochastic loads on the fatigue-crack nucleation time, Submitted, (2021) (26 pages). [Code] [pdf] | Fatigue, Extreme events, Uncertainty quantification |
90. | H. Arbabi, T. Sapsis, Generative stochastic modeling of strongly nonlinear flows with non-Gaussian statistics, Submitted, (2021) (32 pages). [Code] [pdf] | Extreme events, Uncertainty quantification |
89. | 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 equations, Submitted, (2021) (42 pages). [pdf] | Multiscale analysis, Wavelets, Fluid Flows |
88. | 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 sets, Journal of Computational Physics, 442 (2021) 110484 (24 pages). [movie] [pdf] | Uncertainty quantification, Climate modeling, Data-driven modeling |
87. | 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] | Experimental design, Uncertainty quantification, extreme events |
86. | T. Sapsis, Statistics of extreme events in fluid flows and waves, Annual Review of Fluid Mechanics, 53 (2021) 85-111. [free access pdf] | Extreme events, Uncertainty quantification, Review |
85. | A. Blanchard, T. Sapsis, Bayesian optimization with output-weighted optimal sampling, Journal of Computational Physics, 425 (2021) 109901 (16 pages). [Code] [pdf] | Optimization |
84. | S. Rudy, T. Sapsis, Sparse methods for automatic relevance determination, Physica D, 418 (2021) 132843 (16 pages). [Code] [pdf] | Data-driven modeling |
83. | 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). [Code] [pdf] | Extreme events, Uncertainty quantification |
82. | Z. Y. Wan, P. Karnakov, P. Koumoutsakos, T. Sapsis, Bubbles in turbulent flows: Data-driven, kinematic models with history terms, Int. Journal of Multiphase Flows, 129 (2020) 103286 (11 pages). [pdf] | Finite size particles, Data driven modeling |
81. | 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, Int. Journal of Multiphase Flows, 127 (2020) 103262 (8 pages). [pdf] | Finite size particles, Data driven modeling |
80. | Z. 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] | Data driven modeling, Chaotic systems |
79. | A. Athanassoulis, G. Athanassoulis, M. Ptashnyk, T. Sapsis, Strong solutions for the Alber equation and stability of unidirectional wave spectra, Kinetic and Related Models, 13 (2020) 703-737. [pdf] | Extreme events, Nonlinear waves |
78. | N. Aksamit, T. Sapsis, G. Haller, Machine-learning ocean dynamics from Lagrangian drifter trajectories, Journal of Physical Oceanography, 50 (2020) 1179-1196. [pdf] | Finite size particles, Data driven modeling |
77. | 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, (15 pages). [pdf] | Order reduction, Data driven modeling |
76. | S. Guth, T. Sapsis, Machine learning predictors of extreme events occurring in complex dynamical systems, Entropy, 21 (2019) 925 (18 pages). [pdf] | Extreme events, Data driven modeling |
75. | M. Farazmand, T. Sapsis, Closed-loop adaptive control of extreme events in a turbulent flow, Physical Review E, 100 (2019) 033110 (7 pages). [pdf] | Control, Order reduction, Chaotic flows, Extreme events |
74. | 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). [pdf], Editor's Suggestion. | Control, Order reduction, Chaotic flows |
73. | 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] | Extreme events, Uncertainty quantification |
72. | 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] | Extreme events, Order reduction |
69. | 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] | Extreme events, Nonlinear waves |
71. | 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 |
70. | M. Farazmand, T. Sapsis, Surface waves enhance particle dispersion, Fluids, 4 (2019) (12 pages). [pdf] | Water waves, Particles dispersion |
68. | 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] | Control, Order reduction, Chaotic flows |
66. | 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. | Extreme events, Uncertainty quantification |
67. | M. Farazmand, T. Sapsis, Extreme events: mechanisms and prediction, ASME Applied Mechanics Reviews, 71 (2019) 050801. [pdf] | Extreme events, Review |
65. | 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] | Finite size particles, Data driven modeling |
64. | 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] | Extreme events, Review |
63. | 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] | Data driven modeling, Extreme events, Turbulent systems |
62. | 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] | Data driven modeling, Turbulent systems |
61. | 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] | Energy harvesting, Random vibrations |
60. | 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] | Energy harvesting, Random vibrations |
59. | S. Mowlavi, T. Sapsis, Model order reduction for stochastic dynamical systems with continuous symmetries, SIAM Journal on Scientific Computing, 40 (2018) 1669-1695. [pdf] | Uncertainty quantification, Order reduction |
58. | M. Farazmand, T. Sapsis, Physics-based probing and prediction of extreme events, SIAM News, 51 (2018) 1. [link] [pdf] | Extreme events, Nonlinear waves, Turbulent systems |
57. | 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 |
56. | 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] | Extreme events, Random vibrations |
55. | A. Blanchard, T. Sapsis, A. Vakakis, Non-reciprocity in nonlinear elastodynamics, Journal of Sound and Vibration, 412 (2018) 326-335. [pdf] | Nonlinear waves |
54. | M. Farazmand, T. Sapsis, A variational approach to probing extreme events in turbulent dynamical systems, Science Advances, 3:e1701533 (2017) (7 pages). [pdf] | Extreme events, Turbulent systems |
53. | A. Athanassoulis, G. Athanassoulis, T. Sapsis, Localized instabilities of the Wigner equation as a model for the emergence of rogue Waves, J. Ocean Eng. Mar. Energy, 3 (2017) 353-372. [pdf] | Extreme events, Nonlinear waves |
52. | 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] | Extreme events, Random vibrations |
51. | 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] | Extreme events, Order reduction |
50. | J.M. Kluger, A.H. Slocum, and T. Sapsis, Ring-based stiffening flexure applied as a load cell with high resolution and large force range, ASME Journal of Mechanical Design, 139 (2017) 103501 (8 pages). [pdf] | Nonlinear load cells |
49. | M. Farazmand, T. Sapsis, Reduced-order prediction of rogue waves in two dimensional water waves, Journal of Computational Physics, 340 (2017) 418-434. [pdf] | Extreme events, Nonlinear waves |
48. | 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] | Data driven modeling, Turbulent systems |
47. | 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] | Uncertainty quantification, Order reduction |
46. | 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] | Nonlinear vibrations |
45. | 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. | Extreme events, Order reduction, Turbulent systems |
44. | 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] | Extreme events, Nonlinear waves |
43. | 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] | Extreme events, Nonlinear vibrations |
42. | 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. | Extreme events, Order reduction, Turbulent systems |
41. | 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. | Extreme events, Nonlinear waves |
40. | 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] | Uncertainty quantification, Random vibrations |
39. | J. Kluger, T. Sapsis, A. Slocum, A high-resolution and large force-range load cell by means of nonlinear cantilever beams, Precision Engineering, 43 (2016) 241-256. [pdf] | Nonlinear load cells |
38. | 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] | Extreme events, Turbulent systems |
37. | W. Cousins, T. Sapsis, The unsteady evolution of localized unidirectional deep water wave groups, Physical Review E, 91 (2015) 063204 (5 pages). [pdf] | Extreme events, Nonlinear waves |
36. | 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] | Random vibrations, Energy harvesting |
35. | H. -K. Joo, T. Sapsis, Closure schemes for nonlinear bistable systems subjected to correlated Noise: Applications to energy harvesting from water waves, Journal of Ocean and Wind Energy, 2 (2015) 65-72. [pdf] | Random vibrations, Energy harvesting |
34. | A. Petsakou, T. Sapsis, J. Blau, Circadian rhythms in Rho1 activity regulate neuronal plasticity and network hierarchy, Cell, 162 (2015) 1-13. [pdf] | Biology, Geometrical modeling |
33. | A. Majda, D. Qi, T. Sapsis, Blended particle filters for large dimensional chaotic dynamical systems, Proceedings of the National Academy of Sciences, 111 (2014) 7511-7516. [pdf] | Uncertainty quantification, Order reduction, Turbulent systems |
32. | 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] | Extreme events, Nonlinear waves |
31. | 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] | Nonlinear vibrations, Energy harvesting |
30. | 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] | Uncertainty quantification, Order reduction |
29. | 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] | Nonlinear vibrations, Energy harvesting |
28. | 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] | Uncertainty quantification, Order reduction, Turbulent systems |
27. | 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] | Nonlinear vibrations, Energy harvesting |
26. | 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] | Uncertainty quantification, Order reduction, Turbulent systems |
25. | 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] | Uncertainty quantification, Order reduction |
24. | 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] | Uncertainty Quantification, Order reduction, Chaotic flows |
23. | 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] | Uncertainty quantification, Order reduction, Turbulent systems |
22. | 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] | Uncertainty quantification, Order reduction, Chaotic flows |
21. | 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] | Uncertainty quantification, Order reduction, Chaotic flows |
20 | 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] | Uncertainty quantification, Order reduction |
19. | 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] | Uncertainty quantification, Order reduction, Chaotic flows |
18. | 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] | Uncertainty quantification, Correlated excitation |
17. | T. Sapsis & P. Lermusiaux, Dynamical criteria for the evolution of the stochastic dimensionality in flows with uncertainty, Physica D, 241 (2012) 60-76. [pdf] | Uncertainty quantification, Order reduction, Chaotic flows |
16. | 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] | Nonlinear vibrations |
15. | 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] | Finite size particles |
14. | G. Haller & T. Sapsis, Lagrangian coherent structures and the smallest finite-time Lyapunov exponent, Chaos, 21 (2011) 023115 (7 pages). [pdf] | Lagrangian coherent structures |
13. | T. Sapsis, J. Peng, & G. Haller, Instabilities on prey dynamics in jellyfish feeding, Bulletin of Mathematical Biology, 73 (2011) 1841-1856. [pdf] | Finite size particles |
12. | 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] | Nonlinear Vibrations, Random vibrations |
11. | 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] | Nonlinear vibrations |
10. | 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] | Nonlinear vibrations |
9. | T. Sapsis & G. Haller, Clustering criterion for inertial particles in 2D time-periodic and 3D steady flows, Chaos, 20 (2010) 017515 (11 pages). [pdf] | Finite size particles |
8. | G. Haller & T. Sapsis, Localized instability and attraction along invariant manifolds, SIAM Journal of Applied Dynamical Systems, 9 (2010) 611-633. [pdf] | Finite size particles |
7. | T. Sapsis & P. Lermusiaux, Dynamically orthogonal field equations for continuous stochastic dynamical systems, Physica D, 238 (2009) 2347-2360. [pdf] | Uncertainty quantification, Order reduction, Chaotic fluid flows |
6. | T. Sapsis & G. Haller, Inertial particle dynamics in a hurricane, Journal of the Atmospheric Sciences, 66 (2009) 2481-2492. [pdf] | Finite size particles |
5. | 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] | Nonlinear vibrations |
4. | T. Sapsis & G. Haller, Instabilities in the dynamics of neutrally buoyant particles, Physics of Fluids, 20(2008) 017102 (7 pages). [pdf] | Finite size particles |
3. | G. Haller, T. Sapsis, Where do inertial particles go in fluid flows?, Physica D, 237 (2008) 573-583. [pdf] | Finite size particles |
1. | 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] | Uncertainty quantification, Correlated excitation |
2. | 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] | Nonlinear vibrations |