Results 31 to 40 of about 16,533 (261)

Bayesian Reasoning for Physics Informed Neural Networks

CoRR, 2023
21 pages, 12 figures, re-edit the description of the Bayesian framework, some of the content moved to Appendix.
Krzysztof M. Graczyk, Kornel Witkowski
openaire   +2 more sources

IDRLnet: A Physics-Informed Neural Network Library

CoRR, 2021
Physics Informed Neural Network (PINN) is a scientific computing framework used to solve both forward and inverse problems modeled by Partial Differential Equations (PDEs). This paper introduces IDRLnet, a Python toolbox for modeling and solving problems through PINN systematically.
Wei Peng 0010, Jun Zhang 0052, Weien Zhou, Xiaoyu Zhao 0002, Wen Yao 0001, Xiaoqian Chen   +5 more
openaire   +2 more sources

Complex Physics-Informed Neural Network

Journal of Computational Physics
We propose compleX-PINN, a novel physics-informed neural network (PINN) architecture incorporating a learnable activation function inspired by the Cauchy integral theorem. By optimizing the activation parameters, compleX-PINN achieves high accuracy with just a single hidden layer.
Chenhao Si, Ming Yan 0006, Xin Li, Zhihong Xia   +3 more
openaire   +2 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   +1 more source

Meshfree Variational-Physics-Informed Neural Networks (MF-VPINN): An Adaptive Training Strategy

Algorithms
In this paper, we introduce a Meshfree Variational-Physics-Informed Neural Network. It is a Variational-Physics-Informed Neural Network that does not require the generation of the triangulation of the entire domain and that can be trained with an ...
Stefano Berrone, Moreno Pintore
doaj   +1 more source

PND: Physics-informed neural-network software for molecular dynamics applications

SoftwareX, 2021
We have developed PND, a differential equation solver software based on a physics-informed neural network (PINN) for molecular dynamics simulators. Based on automatic differentiation technique provided by PyTorch, our software allows users to flexibly ...
Taufeq Mohammed Razakh, Beibei Wang, Shane Jackson, Rajiv K. Kalia, Aiichiro Nakano, Ken-ichi Nomura, Priya Vashishta   +6 more
doaj   +1 more source

DiffGrad for Physics-Informed Neural Networks

CoRR
20 pages, 14 ...
Jamshaid Ul Rahman, Nimra
openaire   +2 more sources

Parareal with a Physics-Informed Neural Network as Coarse Propagator

, 2023
AbstractParallel-in-time algorithms provide an additional layer of concurrency for the numerical integration of models based on time-dependent differential equations. Methods like Parareal, which parallelize across multiple time steps, rely on a computationally cheap and coarse integrator to propagate information forward in time, while a parallelizable
Abdul Qadir Ibrahim, Sebastian Götschel, Daniel Ruprecht   +2 more
openaire   +2 more sources

Probing optimisation in physics-informed neural networks

CoRR, 2023
Accepted at the ICLR 2023 Workshop on Physics for Machine ...
Nayara Fonseca, Veronica Guidetti, Will Trojak   +2 more
openaire   +2 more sources

Data-driven modeling of Landau damping by physics-informed neural networks

Physical Review Research, 2023
Kinetic approaches are generally accurate in dealing with microscale plasma physics problems but are computationally expensive for large-scale or multiscale systems.
Yilan Qin, Jiayu Ma, Mingle Jiang, Chuanfei Dong, Haiyang Fu, Liang Wang, Wenjie Cheng, Yaqiu Jin   +7 more
doaj   +1 more source