Results 11 to 20 of about 29,946 (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

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

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   +2 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

Physics-Informed Neural Networks in Polymers: A Review [PDF]

Polymers
The modeling and simulation of polymer systems present unique challenges due to their intrinsic complexity and multi-scale behavior. Traditional computational methods, while effective, often struggle to balance accuracy with computational efficiency, especially when bridging the atomistic to macroscopic scales.
Ivan Malashin, Vadim Tynchenko, Andrei Gantimurov, Vladimir Nelyub, Aleksei Borodulin   +4 more
openaire   +3 more sources

Three-Dimensional Thermal Tomography with Physics-Informed Neural Networks [PDF]

Tomography
Background: Accurate reconstruction of internal temperature fields from surface temperature data is critical for applications such as non-invasive thermal imaging, particularly in scenarios involving small temperature gradients, like those in the human ...
Theodoros Leontiou, Anna Frixou, Marios Charalambides, Efstathios Stiliaris, Costas N. Papanicolas, Sofia Nikolaidou, Antonis Papadakis   +6 more
doaj   +2 more sources

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