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Found 5 papers, 1 papers with code
On sparse regression, Lp-regularization, and automated model discovery
With these insights, we demonstrate that Lp regularized constitutive neural networks can simultaneously discover both, interpretable models and physically meaningful parameters.
Viscoelastic Constitutive Artificial Neural Networks (vCANNs) $-$ a framework for data-driven anisotropic nonlinear finite viscoelasticity
These popular models are simplifications that typically cannot accurately capture the nonlinear viscoelastic behavior of materials.
A new family of Constitutive Artificial Neural Networks towards automated model discovery
For more than 100 years, chemical, physical, and material scientists have proposed competing constitutive models to best characterize the behavior of natural and man-made materials in response to mechanical loading.
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems
Our study reveals the inherent advantages and disadvantages of Neural Networks, Bayesian Inference, and a combination of both and provides valuable guidelines for model selection.
Neural Network Surgery: Combining Training with Topology Optimization
Neural architecture search aims to reduce the extent of expertise that is needed.
