Results 21 to 30 of about 2,349 (245)

HOMO-PINN: Hyperparameter Optimization of a Multi-Output Physics-Informed Neural Network

Operations Research Forum
Abstract The good choice of hyperparameters is crucial for the successful application of Deep Learning (DL) networks in order to find accurate solutions or the best parameter in solving Partial Differential Equations (PDEs), that are sensitive to errors in coefficient estimation.
De Rosa M., Pompameo L., Litvinenko A., Cuomo S.   +3 more
openaire   +5 more sources

Correcting model misspecification in physics-informed neural networks (PINNs)

Journal of Computational Physics, 2023
Data-driven discovery of governing equations in computational science has emerged as a new paradigm for obtaining accurate physical models and as a possible alternative to theoretical derivations. The recently developed physics-informed neural networks (PINNs) have also been employed to learn governing equations given data across diverse scientific ...
Zongren Zou, Xuhui Meng, George Em Karniadakis   +2 more
openaire   +3 more sources

Physics-Informed Neural Networks (PINNs) for Parameterized PDEs: A Metalearning Approach [PDF]

SSRN Electronic Journal, 2021
Physics-informed neural networks (PINNs) as a means of discretizing partial differential equations (PDEs) are garnering much attention in the Computational Science and Engineering (CS&E) world. At least two challenges exist for PINNs at present: an understanding of accuracy and convergence characteristics with respect to tunable parameters and ...
Michael Penwarden, Shandian Zhe, Akil Narayan 0001, Robert M. Kirby   +3 more
openaire   +5 more sources

Physics-Informed Neural Network for Unreacted-Core Shrinking Model of Coal Gasification

Chemical Engineering Transactions, 2021
Physics-informed neural network (PINN) has achieved success in many science and engineering disciplines by encoding physical laws into the loss functions of the neural network.
Ming Jian Li, Zengrong Su, Chang He, Bingjian Zhang, Qinglin Chen   +4 more
doaj   +1 more source

Time difference physics-informed neural network for fractional water wave models

Results in Applied Mathematics, 2023
In this article, the time difference physics-informed neural network (TD-PINN) is considered to approximate the solution of fractional water wave models.
Wenkai Liu, Yang Liu, Hong Li
doaj   +1 more source

Application of Physics-Informed Neural Network (PINN) for Understanding Vortex-Induced Vibration with Tunable Stiffness

, 2023
Application of Physics-Informed Neural Network (PINN) for Understanding Vortex-Induced Vibration with Tunable ...
Mujahid Khan
core   +1 more source

EP-PINNs: Cardiac Electrophysiology Characterisation Using Physics-Informed Neural Networks [PDF]

Frontiers in Cardiovascular Medicine, 2022
Accurately inferring underlying electrophysiological (EP) tissue properties from action potential recordings is expected to be clinically useful in the diagnosis and treatment of arrhythmias such as atrial fibrillation. It is, however, notoriously difficult to perform.
Clara Herrero Martin, Clara Herrero Martin, Alon Oved, Rasheda A. Chowdhury, Elisabeth Ullmann, Nicholas S. Peters, Anil A. Bharath, Marta Varela   +7 more
openaire   +7 more sources

Predictive Uncertainty Quantification for Bayesian Physics-Informed Neural Network (Pinn) in Hypocentre Estimation Problem

, 2022
Physics-informed neural networks (PINNs) have appeared on the scene as a flexible and a versatile framework for solving partial differential equations (PDEs), along with any initial or boundary conditions.
Yildirim, I.E., Izzatullah, Muhammad, Alkhalifah, Tariq Ali, Waheed, U.B.   +3 more
core   +1 more source

Spectrum-adaptive physics-informed neural network for rapid ocean acoustic field prediction [PDF]

JASA Express Letters
To address the slow convergence of physics-informed neural networks (PINNs) in ocean acoustics, this work proposes a spectrum-adaptive physics-informed neural network (SA-PINN) built on OceanPINN for efficient modeling.
Yuxiang Gao, Peng Xiao, Zhenglin Li
doaj   +1 more source

Data-driven modeling of Landau damping by physics-informed neural networks

Physical Review Research, 2023
Kinetic approaches are generally accurate in dealing with microscale plasma physics problems but are computationally expensive for large-scale or multiscale systems.
Yilan Qin, Jiayu Ma, Mingle Jiang, Chuanfei Dong, Haiyang Fu, Liang Wang, Wenjie Cheng, Yaqiu Jin   +7 more
doaj   +1 more source