预订Deep Learning For Physics Research 深度学习在物理学研究中的应用,英文原版 举报 作者: Martin Erdmann 出版社: World Scientific Publishing ISBN: 9789811237454 出版时间: 2021-01 装帧: 精装 纸张: 胶版纸 页数: 300页 正文语种: 英文 售价 ￥ 1357.00 品相 九五品 发货 承诺48小时内发货 ...
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。
This announcement highlights NVIDIA's introduction of theMEGA frameworkat CES 2025. The MEGA framework is designed to enable the creation of industrial robot fleet digital twins using NVIDIA Omniverse. This framework leverages advanced 3D modeling, real-time physics, and generative AI to simulate and ...
The source code of the deep-learning algorithm is available for research uses athttps://github.com/liyues/PatRecon. References Download references Acknowledgements This research is partially supported by the National Institutes of Health (R01CA176553 and R01EB016777). The contents of this article...
Deep learning methods have recently shown a broad application prospect in rainfall-runoff modeling. However, the lack of physical mechanism becomes a major limitation in using machine learning methods that rely on the available labeled observations. To address this issue, the study proposes that ...
In recent years, Scientific Machine Learning (SciML) methods for solving Partial Differential Equations (PDEs) have gained increasing popularity. Within such a paradigm, Physics-Informed Neural Networks (PINNs) are novel deep learning frameworks for solving initial-boundary value problems involving nonlinea...
“One belt and one road”innovation cooperation project (BZ2020007) Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense (JSGP202105) Fundamental Research Funds for the Central Universities (30922010405,30921011208,30920032101,30919011222) National Major Scientific Instrument ...
Sam Raymond is a postdoctoral scholar at Stanford University, having completed his Ph.D. in the Center for Computational Science and Engineering (CCSE) at MIT. His research interests include physics-informed machine learning, applying high-perform...
Automated learning from data by means of deep neural networks is finding use in an ever-increasing number of applications, yet key theoretical questions about how it works remain unanswered. A physics-based approach may help to bridge this gap.
Recently, several physics-guided machine learning approaches have been proposed, whereby physical principles are used to inform the search for a physically meaningful and accurate machine learning model. The architecture proposed in [21], for example, enhances the input space of a data-driven system...