1月25日,南京理工大学光电信息科学与工程学院左超教授,陈钱教授,冯世杰副教授,胡岩副教授,香港大学电气电子工程系林彥民教授等组成的团队,在期刊 Opto-Electronic Advances 发表题为 Physics-informed deep learning for fringe pattern analysis 的论文。
The results show that the DNNs are capable of learning physical problems without knowing the underlying fundamental governing equation. The error analysis for various training methods is reported and discussed. The outcomes reveal that DNNs are capable of learning physics, but using MMaSE as a ...
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...
两者对比,deeponet 可以用更少的数据。 52:22 对于复杂问题 可以分块学习然后再整合到一起。 53:52 问题 非线性 模型难以训练 真正的复杂问题难以求解 数据生成 和理论保证。 58:38 细节 先训练数据 再所有
deep learning practice.To this end,we introduce a physics-informed deep learning method for fringe pattern analysis(PI-FPA)to overcome this limit by integrating a lightweight DNN with a learning-enhanced Fourier transform profilometry(LeFTP)module.By para-meterizing conventional phase retrieval methods...
Paper presented at the SPE Reservoir Simulation Conference, On-Demand, October 2021. 这篇论文关注石油储藏模拟问题,应用PINN解决该领域的问题,并对标准PINN进行了改进来适应本领域的问题。显然,本篇论文专业性较强,很多东西我未能理解。因此对于本篇工作我只有一个大概模糊的理解:对于标准PINN,作者从物理规律方面...
读论文 DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills 笔记 一、 文章概览 1.1 动作模仿 1.2 洞见 1.2.1 参考状态初始化(Reference State Initialization,RSI)...
‘Intuitive physics’ enables our pragmatic engagement with the physical world and forms a key component of ‘common sense’ aspects of thought. Current artificial intelligence systems pale in their understanding of intuitive physics, in comparison to ev
关键词 optical metrology deep learning physics-informed neural networks fringe analysis phase retrieval 分类号 TP18 [自动化与计算机技术—控制理论与控制工程] TP391.41 [自动化与计算机技术—计算机应用技术] 相关期刊:《信息对抗技术》 ISSN:2097-163X CN:34-1340/E ...
We introduce physics informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations. We present our developments in the context of solving two main classes of problems...