Comprehensive state-of-the-art book on scientific machine learning approaches in modelling & simulation Covers the wide range of PDEs, uncertainty, optimization, inverse analysis, constitutive modelling & material design

Learn how to simulate complex physical processes in your work using discretization methods and numerical algorithms. Assess and respond to cost-accuracy tradeoffs in simulation and …

In this course, you will learn to simulate physical processes using numerical methods, balance cost-accuracy trade-offs, and apply optimization techniques foundational to machine learning.

Oct 4, 2023 · The objective of this book is to provide an overview of ML in Engineering and the Sciences presenting fundamental theoretical ingredients with a focus on the next generation of computer modeling in Engineering and the Sciences in which the exciting aspects of machine learning are incorporated.

Oct 9, 2023 · The objective of this book is to provide an overview of ML in Engineering and the Sciences presenting fundamental theoretical ingredients with a focus on the next generation of computer modeling in Engineering and the Sciences in which the exciting aspects of machine learning are incorporated.

Jul 5, 2023 · As artificial intelligence (AI), machine learning (ML), and simulation revolutionize the way humans process and predict information, Ansys Chief Technology Officer Dr. Prith Banerjee explains how better, faster decisions are made possible when these transformational sciences align.

In this paper we introduce JuliaSim, a high-performance programming environment designed to blend traditional modeling and simulation with machine learning. JuliaSim can build accelerated surrogates from component-based models, such as those conforming to the FMI standard, using continuous-time echo state networks (CTESN).

Dec 7, 2023 · With machine learning, researchers can detect additional patterns in simulation results, while with simulation, machine learning can get access to more data and added insight into the constraints of the model it’s trying to learn.

Oct 3, 2023 · The objective of this book is to provide an overview of ML in Engineering and the Sciences presenting fundamental theoretical ingredients with a focus on the next generation of computer modeling...

We will develop, analyze, and implement novel ML strategies adapted to the characteristics of simulation data, such as symmetries, invariances, and physical prior knowledge.