tLaSDI: Thermodynamics-informed latent space dynamics identification

arXiv:2403.05848v1 Announce Type: new
Abstract: We propose a data-driven latent space dynamics identification method (tLaSDI) that embeds the first and second principles of thermodynamics. The latent variables are learned through an autoencoder as a nonlinear dimension reduction model. The dynamics of the latent variables are constructed by a neural network-based model that preserves certain structures to respect the thermodynamic laws through the GENERIC formalism. An abstract error estimate of the approximation is established, which provides a new loss formulation involving …

abstract arxiv autoencoder cs.lg data data-driven dynamics identification math.ds network neural network space through type variables