130 citations · 549 across the 28 of their papers we have counts for
3 papers · 2 filters
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…
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…