ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2104.02650
  4. Cited By
Model-data-driven constitutive responses: application to a multiscale
  computational framework

Model-data-driven constitutive responses: application to a multiscale computational framework

6 April 2021
J. Fuhg
C. Boehm
N. Bouklas
A. Fau
P. Wriggers
M. Marino
    AILawAI4CE
ArXiv (abs)PDFHTML

Papers citing "Model-data-driven constitutive responses: application to a multiscale computational framework"

4 / 4 papers shown
Title
EquiNO: A Physics-Informed Neural Operator for Multiscale Simulations
EquiNO: A Physics-Informed Neural Operator for Multiscale Simulations
Hamidreza Eivazi
Jendrik-Alexander Tröger
Stefan H. A. Wittek
Stefan Hartmann
Andreas Rausch
AI4CE
112
1
0
27 Mar 2025
Interval and fuzzy physics-informed neural networks for uncertain fields
Interval and fuzzy physics-informed neural networks for uncertain fields
J. Fuhg
Ioannis Kalogeris
A. Fau
N. Bouklas
AI4CE
97
19
0
18 Jun 2021
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
J. Fuhg
M. Marino
N. Bouklas
80
61
0
07 May 2021
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
PINNAI4CE
81
95
0
15 Apr 2021
1