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Improving the performance of Stein variational inference through extreme
  sparsification of physically-constrained neural network models

Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models

30 June 2024
G. A. Padmanabha
J. Fuhg
Cosmin Safta
Reese E. Jones
N. Bouklas
ArXiv (abs)PDFHTML

Papers citing "Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models"

2 / 2 papers shown
Title
Fully data-driven inverse hyperelasticity with hyper-network neural ODE fields
Vahidullah Tac
Amirhossein Amiri-Hezaveh
Manuel K Rausch
Grace N. Bechtel
F. Sahli Costabal
A. B. Tepole
AI4CE
15
0
0
09 Jun 2025
Uncertainty quantification of neural network models of evolving processes via Langevin sampling
Uncertainty quantification of neural network models of evolving processes via Langevin sampling
Cosmin Safta
Reese E. Jones
Ravi G. Patel
Raelynn Wonnacot
Dan S. Bolintineanu
Craig M. Hamel
S. Kramer
BDL
128
0
0
21 Apr 2025
1