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Local approximate Gaussian process regression for data-driven
  constitutive laws: Development and comparison with neural networks

Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networks

7 May 2021
J. Fuhg
M. Marino
N. Bouklas
ArXivPDFHTML

Papers citing "Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networks"

4 / 4 papers shown
Title
Geometric deep learning for computational mechanics Part II: Graph
  embedding for interpretable multiscale plasticity
Geometric deep learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity
Nikolaos N. Vlassis
WaiChing Sun
AI4CE
32
32
0
30 Jul 2022
NN-EUCLID: deep-learning hyperelasticity without stress data
NN-EUCLID: deep-learning hyperelasticity without stress data
Prakash Thakolkaran
Akshay Joshi
Yiwen Zheng
Moritz Flaschel
L. Lorenzis
Siddhant Kumar
29
98
0
04 May 2022
The mixed deep energy method for resolving concentration features in
  finite strain hyperelasticity
The mixed deep energy method for resolving concentration features in finite strain hyperelasticity
J. Fuhg
N. Bouklas
PINN
AI4CE
28
90
0
15 Apr 2021
Local Gaussian process approximation for large computer experiments
Local Gaussian process approximation for large computer experiments
R. Gramacy
D. Apley
127
391
0
02 Mar 2013
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