Data-driven methods have recently made great progress in the discovery of partial
differential equations (PDEs) from spatial-temporal data. However, several challenges …

A new methodology for rigorous Bayesian learning of high-dimensional stochastic
dynamical models is developed. The methodology performs parallelized computation of …

Robust physics (eg, governing equations and laws) discovery is of great interest for many
engineering fields and explainable machine learning. A critical challenge compared with …

Excerpt This book grew out of multiple stimulating conversations with Nathan Kutz while I
was a postdoc at the University of Washington. It began with joking about taking our favorite …

Robust physics (eg, governing equations and laws) discovery is of great interest for many
engineering fields and explainable machine learning. A critical challenge compared with …

Model discovery aims at autonomously discovering equations underlying a dataset. It is
often approached as a sparse regression problem, selecting terms and constructing the …

Model discovery aims at autonomously discovering differential equations underlying a
dataset. Approaches based on Physics Informed Neural Networks (PINNs) have shown …

Describing the nature with the help of analytical expressions verified through experiments
has been a hallmark of the success of science especially in physics from fundamental law of …