Physicsbased Deep Learning N Thuerey P Holl M Mueller P Schnell by N. Thuerey, P. Holl, M. Mueller, P. Schnell, F. Trost, K. Um instant download after payment.

This document 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, training algorithms tailored to physics problems, 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.

"物理建模和数值模拟“和”基于人工神经网络方法“的组合，利用强大的数值技术上，并在任何可能的地方使用这些技术。协调以数据为中心的观点和物理模拟之间的关系