ResearchTrend.AI
On-site estimation of battery electrochemical parameters via transfer learning based physics-informed neural network approach

On-site estimation of battery electrochemical parameters via transfer learning based physics-informed neural network approach

Josu Yeregui
Iker Lopetegi
Sergio Fernandez
Erik Garayalde
Unai Iraola
ArXiv (abs)PDFHTML

Papers citing "On-site estimation of battery electrochemical parameters via transfer learning based physics-informed neural network approach"

4 / 4 papers shown
Title
An Expert's Guide to Training Physics-informed Neural Networks
An Expert's Guide to Training Physics-informed Neural Networks
Sizhuang He
Shyam Sankaran
Hanwen Wang
P. Perdikaris
PINN
98
108
0
16 Aug 2023
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
Lu Lu
Pengzhan Jin
George Karniadakis
248
2,162
0
08 Oct 2019
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
M. Raissi
P. Perdikaris
George Karniadakis
PINNAI4CE
98
615
0
28 Nov 2017
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
M. Raissi
P. Perdikaris
George Karniadakis
PINNAI4CE
88
932
0
28 Nov 2017

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.