Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …

The integration of data and scientific computation is driving a paradigm shift across the
engineering, natural, and physical sciences. Indeed, there exists an unprecedented …

This book is an introduction to machine learning control (MLC), a surprisingly simple model-
free methodology to tame complex nonlinear systems. These systems are assumed to be …

The various studies of partial differential equations (PDEs) are hot topics of mathematical
research. Among them, solving PDEs is a very important and difficult task. Since many …

In the design of practical combustion systems, ensuring safety and reliability is an important
requirement. For instance, reliably avoiding lean blowout, flame flashback or inlet unstart is …

A method capable of comparing and analyzing the spatio-temporal structures of unsteady
flow fields has not yet been established. Temporal analyses of unsteady flow fields are often …

Dynamic mode decomposition has emerged as a leading technique to identify
spatiotemporal coherent structures from high-dimensional data, benefiting from a strong …

In this paper, deep reinforcement learning (DRL) is applied on forced convection control of
conjugate heat transfer systems governed by the coupled Navier-Stokes and heat transport …

Recently, physics-driven deep learning methods have shown particular promise for the
prediction of physical fields, especially to reduce the dependency on large amounts of pre …

This work offers a discussion on how computational mechanics and physics-informed
machine learning can be integrated into the process of sensing, understanding, and …