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.

翻译：这本数字书包含一个实际和全面的介绍,介绍在物理模拟中与深层次学习有关的一切。尽可能多地,所有主题都以Jupyter笔记本的形式出现手动代码实例,以迅速启动。除了标准的从数据中监督学习外,我们还将研究物理损失制约,更紧密结合的学习算法与不同的模拟,以及强化学习和不确定性建模。我们生活在令人振奋的时代:这些方法在从根本上改变计算机模拟所能实现的目标方面有着巨大的潜力。