Nathan Kutz: Deep Learning Architectures for Science and Engineering

Abstract

Physics based models and governing equations dominate science and engineering practice. The advent of scientific computing has transformed every discipline as complex, high-dimensional and nonlinear systems could be easily simulated using numerical integration schemes whose accuracy and stability could be controlled. With the advent of machine learning, a new paradigm has emerged in computing whereby we can build models directly from data. In this work, integration strategies for leveraging the advantages of both traditional scientific computing and emerging machine learning techniques are discussed. Using domain knowledge and physics-informed principles, new paradigms are available to aid in engineering understanding, design and control.