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Found 7 papers, 2 papers with code
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models
Specifically, $L_0$+SVGD demonstrates superior resilience to noise, the ability to perform well in extrapolated regions, and a faster convergence rate to an optimal solution.
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-dependent response of solids.
Interval and fuzzy physics-informed neural networks for uncertain fields
Partial differential equations involving fuzzy and interval fields are traditionally solved using the finite element method where the input fields are sampled using some basis function expansion methods.
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks
This work is the first to employ and adapt the image-to-image translation concept based on conditional generative adversarial networks (cGAN) towards learning a forward and an inverse solution operator of partial differential equations (PDEs).
Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networks
Hierarchical computational methods for multiscale mechanics such as the FE$^2$ and FE-FFT methods are generally accompanied by high computational costs.
Model-data-driven constitutive responses: application to a multiscale computational framework
Computational multiscale methods for analyzing and deriving constitutive responses have been used as a tool in engineering problems because of their ability to combine information at different length scales.
A machine learning based plasticity model using proper orthogonal decomposition
In order to account for the loading history, the accumulated absolute strain is proposed to be the history variable of the plasticity model.
