Physics-based Deep Learning

09/11/2021
by   Nils Thuerey, et al.
116

This digital book contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations. As much as possible, all topics come with hands-on code examples in the form of Jupyter notebooks to quickly get started. Beyond standard supervised learning from data, we'll look at physical loss constraints, more tightly coupled learning algorithms with differentiable simulations, as well as reinforcement learning and uncertainty modeling. We live in exciting times: these methods have a huge potential to fundamentally change what computer simulations can achieve.

READ FULL TEXT

page 7

page 17

page 19

page 32

page 34

page 35

page 36

page 41

research
11/16/2015

Jet-Images -- Deep Learning Edition

Building on the notion of a particle physics detector as a camera and th...
research
06/17/2019

Iterative Model-Based Reinforcement Learning Using Simulations in the Differentiable Neural Computer

We propose a lifelong learning architecture, the Neural Computer Agent (...
research
01/03/2023

Deep Learning and Computational Physics (Lecture Notes)

These notes were compiled as lecture notes for a course developed and ta...
research
03/03/2021

Learning to Fly – a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control

Robotic simulators are crucial for academic research and education as we...
research
03/13/2021

Hybrid computer approach to train a machine learning system

This book chapter describes a novel approach to training machine learnin...
research
10/05/2020

ξ-torch: differentiable scientific computing library

Physics-informed learning has shown to have a better generalization than...

Please sign up or login with your details

Forgot password? Click here to reset