Results 151 to 160 of about 2,349 (245)

Multi‐Model Ensemble Knowledge Distillation With Physics Constraints for Numerical Weather Prediction Bias Correction

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 3, June 2026.
Abstract Accurate Numerical Weather Prediction (NWP) is of paramount importance for global climate change response and sustainable development. Although numerical models such as the Weather Research and Forecasting (WRF) model are widely applied in operational forecasting, they exhibit significant systematic biases under complex atmospheric conditions,
Juncheng Wu, Zexuan Wang, Haoyu Yu, Shaojie Song   +3 more
wiley   +1 more source

Hard Constraint Projection in a Physics Informed Neural Network

In this work, we embed hard constraints in a physics informed neural network (PINN) which predicts solutions to the 2D incompressible Navier-Stokes equations. We extend the hard constraint method introduced by Chen et al.
Horne, Miranda J. S., Wang, He, Jimack, Peter K., Khan, Amirul   +3 more
core   +1 more source

The Physics-Informed Neural Network Gravity Model: Generation III

Scientific machine learning and the advent of the Physics-Informed Neural Network (PINN) have shown high potential in their ability to solve complex differential equations.
Schaub, Hanspeter, Martin, John
core  

PINN-CHK: Physics-Informed Neural Network for High-Fidelity Prediction of Early-Age Cement Hydration Kinetics

Cement hydration kinetics, characterized by heat generation in early-age concrete, poses a modeling challenge. This work proposes a physics-informed neural network (PINN) named PINN-CHK designed for cement hydration kinetics, to predict early-age ...
Zhang, Tianjie, Rahman, Md Asif, Lu, Yang   +2 more
core  

PHYSICS INFORMED NEURAL NETWORK WITH MULTIDIMENSIONAL WEIGHT CONNECTIONS FOR DIFFERENTIAL EQUATIONS

Recently, Physics-Informed Neural Network (PINN) models has shown as a promising approach for solving various types physical problems which include differential equations.
Khudaybergenov, Kabul, Baxritdinov, Farrux, Jabbarbergen, Kudaybergenov   +2 more
core  

SD-PINN: Physics Informed Neural Networks for Spatially Dependent PDES

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023
Liu, Ruixian, Gerstoft, Peter
openaire   +3 more sources

A Physics-Informed neural network (PINN) for parameter identification in analytical ultracentrifugation (AUC) analysis. [PDF]

J Comput Appl Math
Cao W, Martin R, Demeler B.
europepmc   +1 more source

Comparing Parameter Estimation and State Prediction Performance of Physics Informed Neural Networks in Relation to Bayesian Inference

This thesis investigates two physics informed neural network (PINN) approaches for two mechanistic problems via the performance metrics of model parameter estimation and system state prediction.
Pantano, Michael Francesco
core   +1 more source

A Physics Informed Neural Network (PINN) framework for fractional order modeling of Alzheimer's disease. [PDF]

Front Neuroinform
Mehmood A, Farman M, Afzal F, Nisar KS, Ahmed MA, Hafez M.   +5 more
europepmc   +1 more source

Correction: A Physics Informed Neural Network (PINN) framework for fractional order modeling of Alzheimer's disease. [PDF]

Front Neuroinform
Mehmood A, Farman M, Afzal F, Nisar KS, Ahmed MA, Hafez M.   +5 more
europepmc   +1 more source