Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Vlachas, Pantelis R."'
Machine learning architectures, including transformers and recurrent neural networks (RNNs) have revolutionized forecasting in applications ranging from text processing to extreme weather. Notably, advanced network architectures, tuned for applicatio
Externí odkaz:
http://arxiv.org/abs/2410.02654
Forecasting complex system dynamics, particularly for long-term predictions, is persistently hindered by error accumulation and computational burdens. This study presents RefreshNet, a multiscale framework developed to overcome these challenges, deli
Externí odkaz:
http://arxiv.org/abs/2401.13282
Autor:
Kičić, Ivica, Vlachas, Pantelis R., Arampatzis, Georgios, Chatzimanolakis, Michail, Guibas, Leonidas, Koumoutsakos, Petros
Predictive simulations are essential for applications ranging from weather forecasting to material design. The veracity of these simulations hinges on their capacity to capture the effective system dynamics. Massively parallel simulations predict the
Externí odkaz:
http://arxiv.org/abs/2304.01732
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. To improve the accuracy of prediction
Externí odkaz:
http://arxiv.org/abs/2302.11101
Simulations are vital for understanding and predicting the evolution of complex molecular systems. However, despite advances in algorithms and special purpose hardware, accessing the timescales necessary to capture the structural evolution of bio-mol
Externí odkaz:
http://arxiv.org/abs/2102.08810
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. IMeL consists of two components. The first
Externí odkaz:
http://arxiv.org/abs/2008.10433
Predictive simulations of complex systems are essential for applications ranging from weather forecasting to drug design. The veracity of these predictions hinges on their capacity to capture the effective system dynamics. Massively parallel simulati
Externí odkaz:
http://arxiv.org/abs/2006.13431
Autor:
Kičić, Ivica, Vlachas, Pantelis R., Arampatzis, Georgios, Chatzimanolakis, Michail, Guibas, Leonidas, Koumoutsakos, Petros
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 October 2023 415
Autor:
Vlachas, Pantelis R., Pathak, Jaideep, Hunt, Brian R., Sapsis, Themistoklis P., Girvan, Michelle, Ott, Edward, Koumoutsakos, Petros
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 architect
Externí odkaz:
http://arxiv.org/abs/1910.05266
Dynamical systems with high intrinsic dimensionality are often characterized by extreme events having the form of rare transitions several standard deviations away from the mean. For such systems, order-reduction methods through projection of the gov
Externí odkaz:
http://arxiv.org/abs/1803.03365