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...
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...
The following collection of materials targets"Physics-Based Deep Learning"(PBDL), i.e., the field of methods with combinations of physical modeling and deep learning (DL) techniques. Here, DL will typically refer to methods based on artificial neural networks. The general direction of PBDL repr...
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习(DL)技术相结合的方法领域。这里,DL通常指基于人工神经网络的方法。PBDL的总体方向代表了一个非常活跃且快速发展的研究领域。 在这个领域中,我们可以区分各种不同基于物理的方法,从目标设计、约束、组合方法和...
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 ...
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 ...
Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction Reliab Eng Syst Saf, 182 (2019), pp. 208-218, 10.1016/j.ress.2018.11.011 URL https://www.sciencedirect.com/science/article/pii/S0951832018308299 View PDFView articleGoogle Scholar [39] Yang H...
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...
然后训练智能体通过模仿参考运动来生成更自然的动作。模仿运动数据的模拟在计算机动画中有很长的历史,近期也出现了一些使用深度强化学习的案例,如《DeepLoco: Dynamic Locomotion Skills Using Hierarchical Deep Reinforcement Learning》。尽管结果看起来更加自然,但是离生动再现大量运动还有很远的距离。
However, conventional data-driven machine learning algorithms require heavy inputs of large labeled data, which is computationally expensive for complex and multi-physics problems. In this paper, we proposed a data-free, physics-driven deep learning approach to solve various low-speed flow problems ...