no code implementations • 24 Jan 2024 • Junaid Farooq, Danish Rafiq, Pantelis R. Vlachas, Mohammad Abid Bazaz
Forecasting complex system dynamics, particularly for long-term predictions, is persistently hindered by error accumulation and computational burdens.
1 code implementation • 4 Apr 2023 • Ivica Kičić, Pantelis R. Vlachas, Georgios Arampatzis, Michail Chatzimanolakis, Leonidas Guibas, Petros Koumoutsakos
To the best of our knowledge, AdaLED is the first framework that couples a surrogate model with a computational solver to achieve online adaptive learning of effective dynamics.
no code implementations • 22 Feb 2023 • Pantelis R. Vlachas, Petros Koumoutsakos
Recurrent Neural Networks (RNNs) have become an integral part of modeling and forecasting frameworks in areas like natural language processing and high-dimensional dynamical systems such as turbulent fluid flows.
no code implementations • 17 Feb 2021 • Pantelis R. Vlachas, Julija Zavadlav, Matej Praprotnik, Petros Koumoutsakos
We believe that the proposed framework provides a dramatic increase to simulation capabilities and opens new horizons for the effective modeling of complex molecular systems.
no code implementations • 24 Aug 2020 • Francesco Varoli, Guido Novati, Pantelis R. Vlachas, Petros Koumoutsakos
We propose Improved Memories Learning (IMeL), a novel algorithm that turns reinforcement learning (RL) into a supervised learning (SL) problem and delimits the role of neural networks (NN) to interpolation.
1 code implementation • 24 Jun 2020 • Pantelis R. Vlachas, Georgios Arampatzis, Caroline Uhler, Petros Koumoutsakos
Here we present a novel systematic framework that bridges large scale simulations and reduced order models to Learn the Effective Dynamics (LED) of diverse complex systems.
1 code implementation • 9 Oct 2019 • Pantelis R. Vlachas, Jaideep Pathak, Brian R. Hunt, Themistoklis P. Sapsis, Michelle Girvan, Edward Ott, Petros Koumoutsakos
We examine the efficiency of Recurrent Neural Networks in forecasting the spatiotemporal dynamics of high dimensional and reduced order complex systems using Reservoir Computing (RC) and Backpropagation through time (BPTT) for gated network architectures.
1 code implementation • 9 Mar 2018 • Zhong Yi Wan, Pantelis R. Vlachas, Petros Koumoutsakos, Themistoklis P. Sapsis
In this way, the data-driven model improves the imperfect model in regions where data is available, while for locations where data is sparse the imperfect model still provides a baseline for the prediction of the system dynamics.
Chaotic Dynamics Computational Physics
no code implementations • 21 Feb 2018 • Pantelis R. Vlachas, Wonmin Byeon, Zhong Y. Wan, Themistoklis P. Sapsis, Petros Koumoutsakos
We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks.