7 papers
Beyond Static Models: Hypernetworks for Adaptive and Generalizable Forecasting in Complex Parametric Dynamical Systems
Pantelis R. Vlachas, Konstantinos Vlachas, Eleni Chatzi
Dynamical systems play a key role in modeling, forecasting, and decision-making across a wide range of scientific domains. However, variations in system parameters, also referred t…
Deconstructing Recurrence, Attention, and Gating: Investigating the transferability of Transformers and Gated Recurrent Neural Networks in forecasting of dynamical systems
Hunter S. Heidenreich, Pantelis R. Vlachas, Petros Koumoutsakos
Machine learning architectures, including transformers and recurrent neural networks (RNNs) have revolutionized forecasting in applications ranging from text processing to extreme…
Accelerated Simulations of Molecular Systems through Learning of their Effective Dynamics
Pantelis R. Vlachas, Julija Zavadlav, Matej Praprotnik +1
Simulations are vital for understanding and predicting the evolution of complex molecular systems. However, despite advances in algorithms and special purpose hardware, accessing t…
Improved Memories Learning
Francesco Varoli, Guido Novati, Pantelis R. Vlachas +1
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 netwo…
Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics
Pantelis R. Vlachas, Jaideep Pathak, Brian R. Hunt +4
We examine the efficiency of Recurrent Neural Networks in forecasting the spatiotemporal dynamics of high dimensional and reduced order complex systems using Reservoir Computing (R…
Data-assisted reduced-order modeling of extreme events in complex dynamical systems
Zhong Yi Wan, Pantelis R. Vlachas, Petros Koumoutsakos +1
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. F…