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07/10/2023
NN-EVP: A physics informed neural network-based elasto-viscoplastic framework for predictions of grain size-aware flow response under large deformations
We propose a physics informed, neural network-based elasto-viscoplastici...
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06/07/2022
Deep Convolutional Ritz Method: Parametric PDE surrogates without labeled data
Parametric surrogate models for partial differential equations (PDEs) ar...
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09/19/2021
On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling
Data-driven constitutive modeling is an emerging field in computational ...
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06/18/2021
Interval and fuzzy physics-informed neural networks for uncertain fields
Temporally and spatially dependent uncertain parameters are regularly en...
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05/27/2021
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 translatio...
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05/07/2021
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 ...
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04/06/2021
Model-data-driven constitutive responses: application to a multiscale computational framework
Computational multiscale methods for analyzing and deriving constitutive...
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01/07/2020