Works matching IS 01787675 AND DT 2023 AND VI 72 AND IP 2
Results: 9
A non-intrusive approach for physics-constrained learning with application to fuel cell modeling.
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- Computational Mechanics, 2023, v. 72, n. 2, p. 411, doi. 10.1007/s00466-023-02342-7
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- Article
Convolution Hierarchical Deep-learning Neural Networks (C-HiDeNN): finite elements, isogeometric analysis, tensor decomposition, and beyond.
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- Computational Mechanics, 2023, v. 72, n. 2, p. 333, doi. 10.1007/s00466-023-02336-5
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- Article
Error estimates and physics informed augmentation of neural networks for thermally coupled incompressible Navier Stokes equations.
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- Computational Mechanics, 2023, v. 72, n. 2, p. 267, doi. 10.1007/s00466-023-02334-7
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- Article
Convolution Hierarchical Deep-Learning Neural Network Tensor Decomposition (C-HiDeNN-TD) for high-resolution topology optimization.
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- Computational Mechanics, 2023, v. 72, n. 2, p. 363, doi. 10.1007/s00466-023-02333-8
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- Article
Convolution hierarchical deep-learning neural network (C-HiDeNN) with graphics processing unit (GPU) acceleration.
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- Computational Mechanics, 2023, v. 72, n. 2, p. 383, doi. 10.1007/s00466-023-02329-4
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- Article
Mallat Scattering Transformation based surrogate for Magnetohydrodynamics.
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- Computational Mechanics, 2023, v. 72, n. 2, p. 291, doi. 10.1007/s00466-023-02302-1
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- Article
Deep Learning Discrete Calculus (DLDC): a family of discrete numerical methods by universal approximation for STEM education to frontier research.
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- Computational Mechanics, 2023, v. 72, n. 2, p. 311, doi. 10.1007/s00466-023-02292-0
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- Article
A continuous convolutional trainable filter for modelling unstructured data.
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- Computational Mechanics, 2023, v. 72, n. 2, p. 253, doi. 10.1007/s00466-023-02291-1
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- Article
A structure-preserving neural differential operator with embedded Hamiltonian constraints for modeling structural dynamics.
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- Computational Mechanics, 2023, v. 72, n. 2, p. 241, doi. 10.1007/s00466-023-02288-w
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- Article