Results 11 to 20 of about 8,084,029 (292)

Physics-informed neural networks for diffraction tomography

Advanced Photonics, 2022
We propose a physics-informed neural network (PINN) as the forward model for tomographic reconstructions of biological samples. We demonstrate that by training this network with the Helmholtz equation as a physical loss, we can predict the scattered field accurately. It will be shown that a pretrained network can be fine-tuned for different samples and
Amirhossein Saba, Carlo Gigli, Ahmed Bassam Ayoub, Demetri Psaltis   +3 more
core   +6 more sources

Multi-Step Physics-Informed Deep Operator Neural Network for Directly Solving Partial Differential Equations

Applied Sciences
This paper establishes a method for solving partial differential equations using a multi-step physics-informed deep operator neural network. The network is trained by embedding physics-informed constraints.
Jing Wang, Yubo Li, Anping Wu, Zheng Chen, Jun Huang, Qingfeng Wang, Feng Liu   +6 more
doaj   +2 more sources

Quantum Physics Informed Neural Networks

The 53rd International Conference on Parallel Processing Workshops
In this study, the PennyLane quantum device simulator was used to investigate quantum and hybrid, quantum/classical physics-informed neural networks (PINNs) for solutions to both transient and steady-state, 1D and 2D partial differential equations. The comparative expressibility of the purely quantum, hybrid and classical neural networks is discussed ...
Pratibha Raghupati Hegde, Stefano Markidis   +1 more
core   +7 more sources

FastLRNR and Sparse Physics Informed Backpropagation

Results in Applied Mathematics
We introduce Sparse Physics Informed Backpropagation (SPInProp), a new class of methods for accelerating backpropagation for a specialized neural network architecture called Low Rank Neural Representation (LRNR).
Woojin Cho, Kookjin Lee, Noseong Park, Donsub Rim, Gerrit Welper   +4 more
doaj   +2 more sources

Development in metal multiaxial fatigue life prediction based on physics-informed neural network

Jixie qiangdu
The research on multiaxial fatigue life prediction of materials is one of the critical elements in ensuring the structural integrity of components. In recent years, machine learning, especially neural networks, has been widely applied in fatigue life ...
ZHANG Zhuanli, SUN Xingyue, CHEN Xu
doaj   +2 more sources

Automatic network structure discovery of physics informed neural networks via knowledge distillation

Nature Communications
Partial differential equations (PDEs) are fundamental for modeling complex physical processes, often exhibiting structural features such as symmetries and conservation laws.
Ziti Liu, Yang Liu, Xunshi Yan, Wen Liu, Han Nie, Shuaiqi Guo, Chen-an Zhang   +6 more
doaj   +3 more sources

Conditional physics informed neural networks

Communications in Nonlinear Science and Numerical Simulation, 2022
18 pages, 11 ...
Alexander Kovacs, Lukas Exl, Alexander Kornell, Johann Fischbacher, Markus Hovorka, Markus Gusenbauer, Leoni Breth, Harald Oezelt, Masao Yano, Noritsugu Sakuma, Akihito Kinoshita, Tetsuya Shoji, Akira Kato, Thomas Schrefl   +13 more
openaire   +4 more sources

Physics-informed neural network for mapping vascular and tissue dynamics using laser speckle contrast imaging. [PDF]

Neurophotonics
We present a physics-informed neural network that reconstructs vascular and tissue dynamics from LSCI. Validated in in vivo mouse stroke models, the method matches conventional curve fitting while reducing computation time from hours to seconds.
Li S, Tang R, Liu B, Krepulec V, Donovan W, Boas D, Cheng X, Tian L.   +7 more
europepmc   +2 more sources

Separable Physics-Informed Neural Networks

Advances in Neural Information Processing Systems 36, 2023
Physics-informed neural networks (PINNs) have recently emerged as promising data-driven PDE solvers showing encouraging results on various PDEs. However, there is a fundamental limitation of training PINNs to solve multi-dimensional PDEs and approximate highly complex solution functions.
Junwoo Cho, Seungtae Nam, Hyunmo Yang, Seok-Bae Yun, Youngjoon Hong, Eunbyung Park   +5 more
openaire   +3 more sources

Energy-based error bound of physics-informed neural network solutions in elasticity [PDF]

, 2022
An energy-based a posteriori error bound is proposed for the physics-informed neural network solutions of elasticity problems. An admissible displacement-stress solution pair is obtained from a mixed form of physics-informed neural networks, and the ...
Guo, Mengwu, Haghighat, Ehsan, Mengwu Guo, Ehsan Haghighat   +3 more
core   +1 more source