Results 41 to 50 of about 80,482 (270)
A Taxonomic Survey of Physics-Informed Machine Learning
Applied Sciences, 2023 Physics-informed machine learning (PIML) refers to the emerging area of extracting physically relevant solutions to complex multiscale modeling problems lacking sufficient quantity and veracity of data with learning models informed by physically relevant
Joseph Pateras +2 more
doaj +1 more source
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 +5 more
openaire +2 more sources
Complex Physics-Informed Neural Network
Journal of Computational PhysicsWe 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 +3 more
openaire +2 more sources
Sub-grid modelling for two-dimensional turbulence using neural networks [PDF]
In this investigation, a data-driven turbulence closure framework is introduced and deployed for the sub-grid modelling of Kraichnan turbulence. The novelty of the proposed method lies in the fact that snapshots from high-fidelity numerical data are used
Maulik, Romit +3 more
core +1 more source
DiffGrad for Physics-Informed Neural Networks
CoRR20 pages, 14 ...
Jamshaid Ul Rahman, Nimra
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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 +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 +2 more
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PDE-NetGen 1.0: from symbolic partial differential equation (PDE) representations of physical processes to trainable neural network representations [PDF]
Geoscientific Model Development, 2020 Bridging physics and deep learning is a topical challenge. While deep learning frameworks open avenues in physical science, the design of physically consistent deep neural network architectures is an open issue.
O. Pannekoucke, R. Fablet
doaj +1 more source
Physics-informed neural networks for diffraction tomography
Advanced Photonics, 2022 We propose a physics-informed neural network 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.
Amirhossein Saba +3 more
openaire +2 more sources
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Pediatric sarcomas are a heterogeneous group of tumors that contribute disproportionately to cancer mortality in children. Although congenital anomalies are among the strongest known risk factors for childhood cancer, the risk of specific sarcoma subtypes among affected individuals has not yet been thoroughly evaluated. Procedure We
Russ Wolters +17 more
wiley +1 more source