Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics
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 and …
recent years due to its ability to easily incorporate physical and mechanistic constraints and …
[HTML][HTML] Viscoelasticty with physics-augmented neural networks: Model formulation and training methods without prescribed internal variables
We present an approach for the data-driven modeling of nonlinear viscoelastic materials at
small strains which is based on physics-augmented neural networks (NNs) and requires …
small strains which is based on physics-augmented neural networks (NNs) and requires …
[HTML][HTML] Versatile data-adaptive hyperelastic energy functions for soft materials
S Wiesheier, MA Moreno-Mateos… - Computer Methods in …, 2024 - Elsevier
Applications of soft materials are customarily linked to complex deformation scenarios and
material nonlinearities. In the bioengineering field, soft materials typically mimic the low …
material nonlinearities. In the bioengineering field, soft materials typically mimic the low …
NN-EVP: A physics informed neural network-based elasto-viscoplastic framework for predictions of grain size-aware flow response
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 …
constitutive modeling framework for predicting the flow response in metals as a function of …
A review on data-driven constitutive laws for solids
This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …
surrogate, or emulate constitutive laws that describe the path-independent and path …
Inverse Physics-Informed Neural Networks for transport models in porous materials
Physics-Informed Neural Networks (PINN) are a machine learning tool that can be used to
solve direct and inverse problems related to models described by Partial Differential …
solve direct and inverse problems related to models described by Partial Differential …
Visco-Hyperelastic Constitutive Modeling for High-Damping Rubber Materials During Combined Quasi-Static Compression–Cyclic Shear Deformation Process
B Chen, J Dai - International Journal of Applied Mechanics, 2024 - World Scientific
High-damping rubber materials utilized in high-damping rubber isolation bearings are
frequently subjected to multiple deformations during the occurrence of earthquakes …
frequently subjected to multiple deformations during the occurrence of earthquakes …