Interface PINNs (I-PINNs): A physics-informed neural networks framework for interface problems
We present a novel physics-informed neural networks (PINNs) framework for modeling
interface problems, termed Interface PINNs (I-PINNs). I-PINNs uses different neural networks …
interface problems, termed Interface PINNs (I-PINNs). I-PINNs uses different neural networks …
Interpreting and generalizing deep learning in physics-based problems with functional linear models
Although deep learning has achieved remarkable success in various scientific machine
learning applications, its opaque nature poses concerns regarding interpretability and …
learning applications, its opaque nature poses concerns regarding interpretability and …
Transfer learning‐based physics‐informed neural networks for magnetostatic field simulation with domain variations
JR Lippert, M von Tresckow… - … Journal of Numerical …, 2024 - Wiley Online Library
Physics‐informed neural networks (PINNs) provide a new class of mesh‐free methods for
solving differential equations. However, due to their long training times, PINNs are currently …
solving differential equations. However, due to their long training times, PINNs are currently …
[HTML][HTML] Modeling fluid flow in heterogeneous porous media with physics-informed neural networks: Weighting strategies for the mixed pressure head-velocity …
Physics-informed neural networks (PINNs) are receiving increased attention in modeling
flow in porous media because they can surpass purely data-driven approaches. However, in …
flow in porous media because they can surpass purely data-driven approaches. However, in …
[HTML][HTML] Performance of Fourier-based activation function in physics-informed neural networks for patient-specific cardiovascular flows
Abstract Background and Objectives Physics-informed neural networks (PINNs) can be used
to inversely model complex physical systems by encoding the governing partial differential …
to inversely model complex physical systems by encoding the governing partial differential …
Thermal conductivity estimation using Physics-Informed Neural Networks with limited data
J Jo, Y Jeong, J Kim, J Yoo - Engineering Applications of Artificial …, 2024 - Elsevier
A modified physics-informed neural network (PINN) tailored for solving inverse problems in
data-driven engineering applications was demonstrated. The inherited PINN framework …
data-driven engineering applications was demonstrated. The inherited PINN framework …
Predicting bifurcation and amplitude death characteristics of thermoacoustic instabilities from PINNs-derived van der Pol oscillators
M Xie, X Zhao, D Zhao, J Fu, C Shelton… - Journal of Fluid …, 2024 - cambridge.org
Self-sustained thermoacoustic oscillations as observed in low-emission combustion-
involved gas turbines and aero-engines involve complicated thermal fluid–acoustics …
involved gas turbines and aero-engines involve complicated thermal fluid–acoustics …
A peridynamic-informed deep learning model for brittle damage prediction
R Eghbalpoor, A Sheidaei - Theoretical and Applied Fracture Mechanics, 2024 - Elsevier
Abstract Physics-informed Neural Network (PINN) has been introduced recently to predict
and understand complex physical phenomena by directly incorporating feedback from …
and understand complex physical phenomena by directly incorporating feedback from …
[HTML][HTML] Closed-Boundary Reflections of Shallow Water Waves as an Open Challenge for Physics-Informed Neural Networks
Physics-informed neural networks (PINNs) have recently emerged as a promising
alternative to traditional numerical methods for solving partial differential equations (PDEs) …
alternative to traditional numerical methods for solving partial differential equations (PDEs) …
Physically Informed Synchronic-adaptive Learning for Industrial Systems Modeling in Heterogeneous Media with Unavailable Time-varying Interface
A Wang, P Qin, XM Sun - arXiv preprint arXiv:2401.14609, 2024 - arxiv.org
Partial differential equations (PDEs) are commonly employed to model complex industrial
systems characterized by multivariable dependence. Existing physics-informed neural …
systems characterized by multivariable dependence. Existing physics-informed neural …