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Physics-Based Deep Learning Book
Welcome … Contents Welcome …# Welcome to the Physics-based Deep Learning Book (v0.2) 👋 TL;DR: 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.
TL;DR: This document contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations. E.g., we’ll demonstrate how to circumvent the convergence problems of standard reinforcement learning techniques by leveraging simulators in the training loop. We’ll also discuss the importance of inversion for the update steps, and how higher-order information can be used to speed up convergence, and obtain more accurate neural networks.
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