Results 11 to 20 of about 6,454,505 (250)
Physics-informed neural networks for diffraction tomography
, 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 ...
Demetri Psaltis (2041207) +3 more
core +2 more sources
Scientific Reports, 2023 Estimation of Remaining Useful Lifetime (RUL) of discrete power electronics is important to enable predictive maintenance and ensure system safety. Conventional data-driven approaches using neural networks have been applied to address this challenge ...
Zhonghai Lu +3 more
doaj +1 more source
, 2023 Bridging Physics and AI: The Power of Physics-Informed Neural Networks (PINNs) in Experimental ...
Kamran Mathias
core +1 more source
On physics-informed neural networks for quantum computers
Frontiers in Applied Mathematics and Statistics, 2022 Physics-Informed Neural Networks (PINN) emerged as a powerful tool for solving scientific computing problems, ranging from the solution of Partial Differential Equations to data assimilation tasks.
Stefano Markidis
doaj +1 more source
Physics-informed neural networks for spherical indentation problems
Materials & Design, 2023 A scientific deep learning (SciDL) approach was developed by integrating a regression-based spherical indentation method with an artificial neural network (ANN) to extract elastic–plastic properties from indentation load-depth curves.
Karuppasamy Pandian Marimuthu +1 more
doaj +1 more source
Physics-informed neural networks for solving nonlinear diffusivity and Biot's equations.
PLoS ONE, 2020 This paper presents the potential of applying physics-informed neural networks for solving nonlinear multiphysics problems, which are essential to many fields such as biomedical engineering, earthquake prediction, and underground energy harvesting ...
Teeratorn Kadeethum +2 more
doaj +1 more source
Frontiers in Materials, 2022 (Artificial) neural networks have become increasingly popular in mechanics and materials sciences to accelerate computations with model order reduction techniques and as universal models for a wide variety of materials. However, the major disadvantage of
Arnd Koeppe +6 more
doaj +1 more source
Physics-informed neural networks for modeling astrophysical shocks
Machine Learning: Science and Technology, 2023 Physics-informed neural networks (PINNs) are machine learning models that integrate data-based learning with partial differential equations (PDEs).
S P Moschou +5 more
doaj +1 more source
Physics-informed neural networks for voltage and SOC estimation
, 2022 This work shows benefits of applying simple physics-informed methods to neural networks for battery electrical response ...
Forde, Andrew
core +1 more source
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