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Found 7 papers, 4 papers with code
Theory and implementation of inelastic Constitutive Artificial Neural Networks
As the design of the network is not limited to visco-elasticity, our vision is that the iCANN will reveal to us new ways to find the various inelastic phenomena hidden in the data and to understand their interaction.
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.
Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression
Specifically, we integrate physics informed neural networks (PINNs) and symbolic regression to discover a reaction-diffusion type partial differential equation for tau protein misfolding and spreading.
Utilising physics-guided deep learning to overcome data scarcity
Deep learning (DL) relies heavily on data, and the quality of data influences its performance significantly.
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.
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models
In an application to cardiac electrophysiology, the multi-fidelity classifier achieves an F1 score, the harmonic mean of precision and recall, of 99. 6% compared to 74. 1% of a single-fidelity classifier when both are trained with 50 samples.
