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PINN surrogate of Li-ion battery models for parameter inference. Part
  II: Regularization and application of the pseudo-2D model
v1v2 (latest)

PINN surrogate of Li-ion battery models for parameter inference. Part II: Regularization and application of the pseudo-2D model

28 December 2023
M. Hassanaly
Peter J. Weddle
Ryan N. King
Subhayan De
Alireza Doostan
Corey R. Randall
Eric J. Dufek
Andrew M. Colclasure
Kandler Smith
ArXiv (abs)PDFHTML

Papers citing "PINN surrogate of Li-ion battery models for parameter inference. Part II: Regularization and application of the pseudo-2D model"

9 / 9 papers shown
Title
PINN surrogate of Li-ion battery models for parameter inference. Part I:
  Implementation and multi-fidelity hierarchies for the single-particle model
PINN surrogate of Li-ion battery models for parameter inference. Part I: Implementation and multi-fidelity hierarchies for the single-particle model
M. Hassanaly
Peter J. Weddle
Ryan N. King
Subhayan De
Alireza Doostan
Corey R. Randall
Eric J. Dufek
Andrew M. Colclasure
Kandler Smith
50
9
0
28 Dec 2023
Learning from Integral Losses in Physics Informed Neural Networks
Learning from Integral Losses in Physics Informed Neural Networks
Ehsan Saleh
Saba Ghaffari
Timothy Bretl
Luke N. Olson
Matthew West
PINNAI4CE
70
4
0
27 May 2023
Physics-Informed Deep Neural Operator Networks
Physics-Informed Deep Neural Operator Networks
S. Goswami
Aniruddha Bora
Yue Yu
George Karniadakis
PINNAI4CE
87
106
0
08 Jul 2022
Adversarial sampling of unknown and high-dimensional conditional
  distributions
Adversarial sampling of unknown and high-dimensional conditional distributions
M. Hassanaly
Andrew Glaws
Karen Stengel
Ryan N. King
GAN
59
21
0
08 Nov 2021
Estimating State of Charge for xEV batteries using 1D Convolutional
  Neural Networks and Transfer Learning
Estimating State of Charge for xEV batteries using 1D Convolutional Neural Networks and Transfer Learning
A. Bhattacharjee
Ashu Verma
S. Mishra
T. Saha
21
99
0
02 Nov 2020
Composable Effects for Flexible and Accelerated Probabilistic
  Programming in NumPyro
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro
Du Phan
Neeraj Pradhan
M. Jankowiak
58
358
0
24 Dec 2019
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
2,131
0
08 Oct 2019
Automatic differentiation in machine learning: a survey
Automatic differentiation in machine learning: a survey
A. G. Baydin
Barak A. Pearlmutter
Alexey Radul
J. Siskind
PINNAI4CEODL
166
2,808
0
20 Feb 2015
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian
  Monte Carlo
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
Matthew D. Hoffman
Andrew Gelman
165
4,304
0
18 Nov 2011
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