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08/21/2023
Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen
Data-driven constitutive modeling frameworks based on neural networks an...
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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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04/27/2023
Physics-informed Data-driven Discovery of Constitutive Models with Application to Strain-Rate-sensitive Soft Materials
A novel data-driven constitutive modeling approach is proposed, which co...
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10/15/2022
Modular machine learning-based elastoplasticity: generalization in the context of limited data
The development of accurate constitutive models for materials that under...
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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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02/11/2022
Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning
We propose a unified data-driven reduced order model (ROM) that bridges ...
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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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04/15/2021
The mixed deep energy method for resolving concentration features in finite strain hyperelasticity
The introduction of Physics-informed Neural Networks (PINNs) has led to ...
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04/06/2021