高精度数据和低精度数据通过独特设计的网络结构,把两个数据同时用到一起。 47:54 deeponet 和传统方法 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...
内容提示: Physics Informed Deep Learning (Part I): Data-drivenSolutions of Nonlinear Partial Dif f erential EquationsMaziar Raissi 1 , Paris Perdikaris 2 , and George Em Karniadakis 11 Division of Applied Mathematics, Brown University,Providence, RI, 02912, USA2 Department of Mechanical Engineering...
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。
Another limitation of the conventional decomposition process – informed purely by the physics of photoelectric effect and Compton scattering – is that it ignores the anatomic context and location of a voxel when processing low and high-energy slices. The elemental composition of a voxel, and conseq...
In this work, a physics-informed neural network (PINN) designed specifically for analyzing digital materials is introduced. This proposed machine learning (ML) model can be trained free of ground truth data by adopting the minimum energy criteria as its loss function. Results show that our energy...
Physics Informed Deep Learning Data-driven solutions and discovery of Nonlinear Partial Differential Equations View on GitHub Authors Maziar Raissi,Paris Perdikaris, andGeorge Em Karniadakis Abstract We introduce physics informed neural networks – neural networks that are trained to solve supervised learnin...
Raissi, Maziar, Paris Perdikaris, and George Em Karniadakis. "Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations." arXiv preprint arXiv:1711.10566 (2017). Citation @article{raissi2019physics, title={Physics-informed neural networks: A deep l...
Figure 1. Fractional flow curve (blue) along with Welge construction (dotted black) for case with Swc=Sor= 0. Source:Physics Informed Deep Learning for Flow and Transport in Porous Media Until now, no other known method solved such a problem using a sampling method, so this remained an ope...
PINNs 被提出后，其相关模型一直被应用于求解各种NPDE，例如未知解的函数逼近和数据动发现。值得注意的是...
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations - maziarraissi/PINNs