| Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks PR Vlachas, W Byeon, ZY Wan, TP Sapsis, P Koumoutsakos Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2018 | 570 | 2018 |
| Backpropagation algorithms and reservoir computing in recurrent neural networks for the forecasting of complex spatiotemporal dynamics PR Vlachas, J Pathak, BR Hunt, TP Sapsis, M Girvan, E Ott, ... Neural Networks 126, 191-217, 2020 | 437 | 2020 |
| Data-assisted reduced-order modeling of extreme events in complex dynamical systems ZY Wan, P Vlachas, P Koumoutsakos, T Sapsis PloS one 13 (5), e0197704, 2018 | 262 | 2018 |
| Multiscale simulations of complex systems by learning their effective dynamics PR Vlachas, G Arampatzis, C Uhler, P Koumoutsakos Nature Machine Intelligence 4 (4), 359-366, 2022 | 147 | 2022 |
| Accelerated simulations of molecular systems through learning of effective dynamics PR Vlachas, J Zavadlav, M Praprotnik, P Koumoutsakos Journal of Chemical Theory and Computation 18 (1), 538-549, 2021 | 43 | 2021 |
| Forecasting of spatio-temporal chaotic dynamics with recurrent neural networks: A comparative study of reservoir computing and backpropagation algorithms PR Vlachas, J Pathak, BR Hunt, TP Sapsis, M Girvan, E Ott, ... arXiv preprint arXiv:1910.05266, 2019 | 37 | 2019 |
| Adaptive learning of effective dynamics for online modeling of complex systems I Kičić, PR Vlachas, G Arampatzis, M Chatzimanolakis, L Guibas, ... Computer Methods in Applied Mechanics and Engineering 415, 116204, 2023 | 22* | 2023 |
| Learning the effective dynamics of complex multiscale systems PR Vlachas, G Arampatzis, C Uhler, P Koumoutsakos arXiv preprint arXiv:2006.13431, 2020 | 10 | 2020 |
| Learning from predictions: fusing training and autoregressive inference for long-term spatiotemporal forecasts PR Vlachas, P Koumoutsakos arXiv preprint arXiv:2302.11101, 2023 | 7 | 2023 |
| Deconstructing recurrence, attention, and gating: Investigating the transferability of transformers and gated recurrent neural networks in forecasting of dynamical systems HS Heidenreich, PR Vlachas, P Koumoutsakos arXiv preprint arXiv:2410.02654, 2024 | 4 | 2024 |
| Learning and forecasting the effective dynamics of complex systems across scales PR Vlachas ETH Zurich, 2022 | 3 | 2022 |
| A fast analytical approach for static power-down mode analysis M Zwerger, PR Vlachas, H Graeb 2015 IEEE International Conference on Electronics, Circuits, and Systems …, 2015 | 2 | 2015 |
| RefreshNet: learning multiscale dynamics through hierarchical refreshing J Farooq, D Rafiq, PR Vlachas, MA Bazaz Nonlinear Dynamics, 1-18, 2024 | 1 | 2024 |
| Improved Memories Learning F Varoli, G Novati, PR Vlachas, P Koumoutsakos arXiv preprint arXiv:2008.10433, 2020 | 1 | 2020 |
| Alanine dipeptide data PR Vlachas ETH Zurich, Computational Science & Engineering Laboratory, 2021 | | 2021 |
| Distributional Reinforcement Learning PR Vlachas | | 2019 |
| 2 Publications and presentations P Vlachas, W Byeon, ZY Wan, T Sapsis, P Koumoutsakos dynamical systems 3, e1701533, 2017 | | 2017 |
| A Comparison of ADMM and AMA for MPC P Vlachas Automatic Control lab (IfA), ETH Zurich, 2016 | | 2016 |
| Session A1L-A: Analog Circuit Techniques I TRTNC Assessment | | |
Petros KoumoutsakosHarvard UniversityAdresse e-mail validée de seas.harvard.edu
