Using Neural Implicit Flow To Represent Latent Dynamics Of Canonical Systems

arXiv:2404.17535v1 Announce Type: new
Abstract: The recently introduced class of architectures known as Neural Operators has emerged as highly versatile tools applicable to a wide range of tasks in the field of Scientific Machine Learning (SciML), including data representation and forecasting. In this study, we investigate the capabilities of Neural Implicit Flow (NIF), a recently developed mesh-agnostic neural operator, for representing the latent dynamics of canonical systems such as the Kuramoto-Sivashinsky (KS), forced Korteweg-de Vries (fKdV), and Sine-Gordon (SG) equations, …

abstract architectures arxiv canonical capabilities class cs.ai cs.lg data dynamics flow forecasting machine machine learning operators representation scientific study systems tasks tools type