Welcome … — Physics-based Deep Learning

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. 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.

Welcome … # Welcome to the Physics-based Deep Learning Book (v0.3, the GenAI edition) 👋 TL;DR : This document is a hands-on, comprehensive guide to deep learning in the realm of physical simulations. Rather than just theory, we emphasize practical application: every concept is paired with interactive Jupyter notebooks to get you up and running quickly. Beyond traditional supervised learning, we dive into physical loss-constraints , differentiable simulations, diffusion-based approaches for probabilistic generative AI , as well as reinforcement learning and advanced neural network architecture

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• Dival Banerjee

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