Zobrazeno 1 - 10
of 196
pro vyhledávání: '"Berman, Jules"'
The aim of this work is to learn models of population dynamics of physical systems that feature stochastic and mean-field effects and that depend on physics parameters. The learned models can act as surrogates of classical numerical models to efficie
Externí odkaz:
http://arxiv.org/abs/2410.12000
Autor:
Berman, Jules, Peherstorfer, Benjamin
This work introduces reduced models based on Continuous Low Rank Adaptation (CoLoRA) that pre-train neural networks for a given partial differential equation and then continuously adapt low-rank weights in time to rapidly predict the evolution of sol
Externí odkaz:
http://arxiv.org/abs/2402.14646
Autor:
Golkar, Siavash, Berman, Jules, Lipshutz, David, Haret, Robert Mihai, Gollisch, Tim, Chklovskii, Dmitri B.
To generate actions in the face of physiological delays, the brain must predict the future. Here we explore how prediction may lie at the core of brain function by considering a neuron predicting the future of a scalar time series input. Assuming tha
Externí odkaz:
http://arxiv.org/abs/2401.03248
This work focuses on the conservation of quantities such as Hamiltonians, mass, and momentum when solution fields of partial differential equations are approximated with nonlinear parametrizations such as deep networks. The proposed approach builds o
Externí odkaz:
http://arxiv.org/abs/2310.07485
Autor:
Berman, Jules, Peherstorfer, Benjamin
Training neural networks sequentially in time to approximate solution fields of time-dependent partial differential equations can be beneficial for preserving causality and other physics properties; however, the sequential-in-time training is numeric
Externí odkaz:
http://arxiv.org/abs/2310.04867