130 citations · 549 across the 28 of their papers we have counts for
4 papers · 1 filter
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics
Jan N. Fuhg, Reese E. Jones, Nikolaos Bouklas
Data-driven constitutive modeling with neural networks has received increased interest in recent years due to its ability to easily incorporate physical and mechanistic constraints…
Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen
Jan N. Fuhg, Nikolaos Bouklas, Reese E. Jones
Data-driven constitutive modeling frameworks based on neural networks and classical representation theorems have recently gained considerable attention due to their ability to easi…
NN-EVP: A physics informed neural network-based elasto-viscoplastic framework for predictions of grain size-aware flow response under large deformations
Adnan Eghtesad, Jan Niklas Fuhg, Nikolaos Bouklas
We propose a physics informed, neural network-based elasto-viscoplasticity (NN-EVP) constitutive modeling framework for predicting the flow response in metals as a function of unde…
Physics-informed Data-driven Discovery of Constitutive Models with Application to Strain-Rate-sensitive Soft Materials
Kshitiz Upadhyay, Jan N. Fuhg, Nikolaos Bouklas +1
A novel data-driven constitutive modeling approach is proposed, which combines the physics-informed nature of modeling based on continuum thermodynamics with the benefits of machin…