Results 11 to 20 of about 18,341 (302)

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, Carlo Gigli, Ahmed B. Ayoub, Demetri Psaltis   +3 more
openaire   +3 more sources

Physics-Informed Neural Networks and Extensions

CoRR
Frontiers of Science Awards ...
Maziar Raissi, Paris Perdikaris, Nazanin Ahmadi Daryakenari, George Em Karniadakis   +3 more
openaire   +3 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

Investigating molecular transport in the human brain from MRI with physics-informed neural networks

Scientific Reports, 2022
In recent years, a plethora of methods combining neural networks and partial differential equations have been developed. A widely known example are physics-informed neural networks, which solve problems involving partial differential equations by ...
Bastian Zapf, Johannes Haubner, Miroslav Kuchta, Geir Ringstad, Per Kristian Eide, Kent-Andre Mardal   +5 more
doaj   +1 more source

Determining COVID-19 Dynamics Using Physics Informed Neural Networks

Axioms, 2022
The Physics Informed Neural Networks framework is applied to the understanding of the dynamics of COVID-19. To provide the governing system of equations used by the framework, the Susceptible–Infected–Recovered–Death mathematical model is used.
Joseph Malinzi, Simanga Gwebu, Sandile Motsa   +2 more
doaj   +1 more source

Physics-Informed Neural Networks for shell structures

European Journal of Mechanics - A/Solids, 2023
ISSN:1873 ...
Jan-Hendrik Bastek, Dennis M. Kochmann
openaire   +2 more sources

Physics-Informed Neural Networks for Pathloss Prediction

2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), 2023
This paper introduces a physics-informed machine learning approach for pathloss prediction. This is achieved by including in the training phase simultaneously (i) physical dependencies between spatial loss field and (ii) measured pathloss values in the field.
Steffen Limmer, Alberto Martínez Alba, Nicola Michailow   +2 more
openaire   +2 more sources

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, Hyungyil Lee   +1 more
doaj   +1 more source

Remaining useful lifetime estimation for discrete power electronic devices using physics-informed neural network

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, Chao Guo, Mingrui Liu, Rui Shi   +3 more
doaj   +1 more source

General implementation of quantum physics-informed neural networks

Array, 2023
Recently, a novel type of Neural Network (NNs): the Physics-Informed Neural Networks (PINNs), was discovered to have many applications in computational physics.
Shashank Reddy Vadyala, Sai Nethra Betgeri   +1 more
doaj   +1 more source