We elucidate how a multi-faceted approach accommodates hybrid data- and physics-driven machine learning for reconstruction of 3D PET/MRI, summarizing important deep learning developments made in the last 5 years to address attenuation correction, scattering, low photon counts, and data consistency. ...
Bridging the Gap: Physics-Driven Deep Learning for Heat Transfer Model of the Heart Tissuedoi:10.1007/978-3-031-71419-1_14The potential of Physics-Informed Neural Networks (PINNs) in addressing intricate real-world challenges exceeds the capabilities of traditional deep learning methods by merging ...
In recent years, deep learning has been proven to be highly effective in addressing phase recovery problems. The two most direct deep learning phase recovery strategies are data-driven (DD) with supervised learning mode and physics-driven (PD) with self-supervised learning mode. DD and PD ...
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations 这篇文章是关于物理学信息神经网络(Physics Informed Neural Networks,简称PINNs)的研究,由Maziar Raissi、Paris Perdikaris和George Em Karniadakis撰写。文章分为两部分,这是第一部分,主要讨论了如何利用PINNs...
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 and Applie...
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. In this two part treatise, we present our developments in the context of solving...
it particularly popular in different application areas, including but not limited to: finding numerical solutions of partial differential equations (PDEs), image processing, computer vision and graphics, deep learning and neural networks, etc... L Ke 被引量: 2发表: 2016年 NeuFENet: neural finite...
Deep learning is currently prompting increasing research interests and leading to a paradigm shift from physics-based modeling to data-driven learning in the field of optical metrology. Scientists in China and Singapore published ...
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...
deep learning; magnetotelluric; inversion; electromagnetics; subsurface imaging1. Introduction The Magnetotelluric (MT) method, as a remote sensing tool for the subsurface, has been widely applied in geophysical prospecting scenarios, such as crust imaging, geothermal exploration, and mineral, oil and ...