Results 171 to 180 of about 2,291 (214)
Robust Physics-Informed Neural Network Approach for Estimating Heterogeneous Elastic Properties from Noisy Displacement Data. [PDF]
Adv Sci (Weinh)Srikitrungruang T +4 more
europepmc +1 more source
A unified spatiotemporal-geometry framework for target classification and localisation in dual-static passive radar. [PDF]
PLoS OneWang H, Lei Z, Liu X.
europepmc +1 more source
Correction: A Physics Informed Neural Network (PINN) framework for fractional order modeling of Alzheimer's disease. [PDF]
Front NeuroinformMehmood A +5 more
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Adaptive transfer learning for PINN
Journal of Computational Physics, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yang Liu 0145 +4 more
openaire +2 more sources
Kalpa Publications in Computing, 2023
The goal of this work is to solve a nonlinear parabolic PDE problem that arise in the financial world by means of the so called PINNs methodology. We propose a novel treat- ment of the boundary conditions that allows us to avoid, as far as possible, the heuristic choice of the weights for the contributions of the boundary addends of the loss function ...
Joel P. Villarino +2 more
openaire +1 more source
The goal of this work is to solve a nonlinear parabolic PDE problem that arise in the financial world by means of the so called PINNs methodology. We propose a novel treat- ment of the boundary conditions that allows us to avoid, as far as possible, the heuristic choice of the weights for the contributions of the boundary addends of the loss function ...
Joel P. Villarino +2 more
openaire +1 more source
This repository contains data for reproducing analysis and results from the work on "Process-informed neural networks: a hybrid modelling approach to improve predictive performance and inference of neural networks in ecology and beyond".
Dormann, Carsten +2 more
openaire +1 more source
Dormann, Carsten +2 more
openaire +1 more source
Interface PINNs (I-PINNs): A physics-informed neural networks framework for interface problems
Computer Methods in Applied Mechanics and EngineeringzbMATH Open Web Interface contents unavailable due to conflicting licenses.
Antareep Kumar Sarma +4 more
openaire +2 more sources
Generalization of PINNs for elliptic interface problems
Applied Mathematics LetterszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xuelian Jiang +3 more
openaire +2 more sources
Convergence Analysis of PINNs with Over-Parameterization
Communications in Computational PhysicsSummary: Recently, physics-informed neural networks (PINNs) have been shown to be a simple and efficient method for solving PDEs empirically. However, the numerical analysis of PINNs is still incomplete, especially why over-parameterized PINNs work remains unknown.
Chen, Mo +5 more
openaire +2 more sources