Dynamics-Encoded Deep Learning for Robust System Identification and Parameter Estimation

ArXi:2410.04299v2 Announce Type: replace Incorporating a priori physics knowledge into machine learning leads to robust and interpretable algorithms. In this work, we combine deep learning techniques and classic numerical methods for differential equations to address two challenging missing physics problems in dynamical systems theory: dynamics discovery and parameter estimation. The presented methods encode available information relating to the system dynamics into deep learning architectures, incorporating different assumptions on the known inputs and desired outputs in each case.