Results 11 to 20 of about 80,482 (270)

Quantum Physics-Informed Neural Networks [PDF]

Entropy
In this study, the PennyLane quantum device simulator was used to investigate quantum and hybrid, quantum/classical physics-informed neural networks (PINNs) for solutions to both transient and steady-state, 1D and 2D partial differential equations.
Corey Trahan, Mark Loveland, Samuel Dent
doaj   +5 more sources

Enforcing Dirichlet boundary conditions in physics-informed neural networks and variational physics-informed neural networks [PDF]

Heliyon, 2023
In this paper, we present and compare four methods to enforce Dirichlet boundary conditions in Physics-Informed Neural Networks (PINNs) and Variational Physics-Informed Neural Networks (VPINNs).
S. Berrone, C. Canuto, M. Pintore, N. Sukumar   +3 more
doaj   +9 more sources

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   +2 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

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 to Model and Control Robots: A Theoretical and Experimental Investigation

Advanced Intelligent Systems
This work concerns the application of physics‐informed neural networks to the modeling and control of complex robotic systems. Achieving this goal requires extending physics‐informed neural networks to handle nonconservative effects. These learned models
Jingyue Liu, Pablo Borja, Cosimo Della Santina   +2 more
doaj   +2 more sources

Conditional physics informed neural networks

Communications in Nonlinear Science and Numerical Simulation, 2022
18 pages, 11 ...
Alexander Kovacs, Lukas Exl, Alexander Kornell, Johann Fischbacher, Markus Hovorka, Markus Gusenbauer, Leoni Breth, Harald Oezelt, Masao Yano, Noritsugu Sakuma, Akihito Kinoshita, Tetsuya Shoji, Akira Kato, Thomas Schrefl   +13 more
openaire   +2 more sources

Protein Design Using Physics Informed Neural Networks

Biomolecules, 2023
The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function.
Sara Ibrahim Omar, Chen Keasar, Ariel J. Ben-Sasson, Eldad Haber   +3 more
doaj   +1 more source

Scalable algorithms for physics-informed neural and graph networks

Data-Centric Engineering, 2022
Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available.
Khemraj Shukla, Mengjia Xu, Nathaniel Trask, George E. Karniadakis   +3 more
doaj   +1 more source

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