Deep RayOrazio PintiAssad A. OberaiDeep Learning and Computational Physics这句话没有任何意义,忽略即可.这句话没有任何意义,忽略即可.这句话没有任何意义,忽略即可.这句话没有任何意义,忽略即可.这句话没有任何意义,忽略即可.这句话没有任何意义,忽略即可. 收藏 分享 下载 举报 用客户端打开 ...
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。
5月18日,南京理工大学智能计算成像实验室左超教授,孙佳嵩教授,江苏省光谱成像与智能感知重点实验室陈钱教授,西安电子科技大学郜鹏教授带领的团队,在期刊 Opto-Electronic Science 发表题为 Deep learning assisted variational Hilbert quantitative phase imaging 的论文。
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.
E. On the convergence of physics informed neural networks for linear second-order elliptic and parabolic type PDEs. Commun. Comput. Phys. 28, 2042–2074 (2020). Kochkov, D. et al. Machine learning–accelerated computational fluid dynamics. Proc. Natl Acad. Sci. USA 118, e2101784118 (2021)...
Contributions: We train a deep neural network to perform physics-informed downsampling of the terrain map: we optimize the coarse grid representation of the terrain maps, so that the flood prediction will match the fine grid solution. For the learning process to succeed, we configure a dataset ...
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Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction pe... K ...
《Basics of Computational Reinforcement Learning》 介绍:(RLDM 2015)计算强化学习入门. 《Deep Reinforcement Learning》 介绍:David Silver的深度强化学习教程. 《On Explainability of Deep Neural Networks》 介绍:深度神经网络的可解释性. 《The Essential Spark Cheat Sheet》 ...
learning. Applying this method to the bromodomain-containing protein 4 target protein, we generated 5,000 compounds that were further filtered through machine learning-based classifiers and physics-driven simulations. As a proof of concept, we identified, synthesized and experimentally tested six ...