Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers , Project: https://github.com/tum-pbs/Solver-in-the-LoopNumerical investigation of minimum drag profiles in laminar flow using deep learning surrogates , PDF: https://arxiv.org/pdf/2009.14339...
这本书的名字Physics-based Deep Learning，基于物理的深度学习，表示“物理建模和数值模拟”与“基于人工神经网络的方法”的组合。目的是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。因此，本书的一个中心目标是，协调以数据为中心的观点与物理模拟之间的关系。由此产生的新方法具有巨大的潜力，可以改进传统...
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 ...
电子书《Physics-based Deep Learning》基于物理的深度学习书籍(v0.2版)👋O网页链接本文档包含了与物理模拟背景下深度学习相关的一切内容的实用和全面介绍。尽可能地,所有主题都附有Jupyter notebook形式的实践代码示例,以便快速入门。除了标准的从数据中进行监督学习,我们还将探讨物理损失约束、与可微分模拟更紧密耦合...
Learning time-dependent PDE solver using Message Passing Graph Neural Networks, arXiv 2022, paper Scalable algorithms for physics-informed neural and graph networks, arXiv 2022, paper Books & Thesis Physics-based Deep Learning, 2021. single-PDF version, online readable version Patrick Kidger, On Ne...
Github正在快速加星的Physics-based Deep Learning新书,“基于物理的深度学习”(PBDL),即物理建模和深度学
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 ...
Values of physical variables that represent a first state of a first physical system are estimated using a deep learning (DL) algorithm that is trained based on values of physical variables that represent states of other physical systems that are determined by one or more physical equations and ...
Although deep learning has achieved remarkable success in various scientific machine learning applications, its opaque nature poses concerns regarding inte
2018[SIG]DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills 本文是Xuebin Peng组在2018年比较出圈的工作,也是Physics-based Animation比较重要的工作,里面的许多设定在后续的AMP、ASE等文章中都有沿用。总体来说这篇工作比较有GCRL的感觉,用一个比较复杂的奖励让角色学一个比较...