Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks.

ABSTRACT: Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equations, thus demanding extensive computational resources. In contrast, data-driven approaches leverage deep learning algorithms to describe system evolution in low-dimensional spaces. We introduce an architecture, termed Latent Dynamics Network, capable of uncovering low-dimensional intrinsic dynamics in potentially non-Markovian systems. Latent Dynamics Networks automatically discover a low-dimensional manifold while learning the system dynamics, eliminating the need for training an auto-encoder and avoiding operations in the high-dimens

SUBMITTER: Regazzoni F 

PROVIDER: S-EPMC11258335 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature