Pages that link to "Item:Q2020954"
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The following pages link to Machine learning materials physics: multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures (Q2020954):
Displaying 26 items.
- Microstructural inelastic fingerprints and data-rich predictions of plasticity and damage in solids (Q785491) (← links)
- Response classification of simple polycrystalline microstructures (Q1013914) (← links)
- Machine learning materials physics: surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics (Q1986728) (← links)
- Machine learning materials physics: integrable deep neural networks enable scale bridging by learning free energy functions (Q1988102) (← links)
- Scale bridging materials physics: active learning workflows and integrable deep neural networks for free energy function representations in alloys (Q2021083) (← links)
- Finite electro-elasticity with physics-augmented neural networks (Q2083132) (← links)
- Towards out of distribution generalization for problems in mechanics (Q2083180) (← links)
- Inside the black box: a physical basis for the effectiveness of deep generative models of amorphous materials (Q2133569) (← links)
- Learning finite element convergence with the multi-fidelity graph neural network (Q2145122) (← links)
- Multiscale modeling of inelastic materials with thermodynamics-based artificial neural networks (TANN) (Q2160403) (← links)
- Microstructure-guided deep material network for rapid nonlinear material modeling and uncertainty quantification (Q2160409) (← links)
- A heteroencoder architecture for prediction of failure locations in porous metals using variational inference (Q2160437) (← links)
- Data-driven tissue mechanics with polyconvex neural ordinary differential equations (Q2160446) (← links)
- Numerical analysis of non-local calculus on finite weighted graphs, with application to reduced-order modeling of dynamical systems (Q2679320) (← links)
- A three-dimensional prediction method of stiffness properties of composites based on deep learning (Q6044224) (← links)
- Accelerated offline setup of homogenized microscopic model for multi‐scale analyses using neural network with knowledge transfer (Q6060946) (← links)
- Solving nonconvex energy minimization problems in martensitic phase transitions with a mesh-free deep learning approach (Q6084532) (← links)
- Automated model discovery for skin: discovering the best model, data, and experiment (Q6094670) (← links)
- Error estimates and physics informed augmentation of neural networks for thermally coupled incompressible Navier Stokes equations (Q6109270) (← links)
- On the micromechanics of deep material networks (Q6115410) (← links)
- Deep learning and multi-level featurization of graph representations of microstructural data (Q6159319) (← links)
- Efficient multiscale modeling of heterogeneous materials using deep neural networks (Q6159331) (← links)
- Embedding physical knowledge in deep neural networks for predicting the phonon dispersion curves of cellular metamaterials (Q6159334) (← links)
- Neural cellular automata for solidification microstructure modelling (Q6171252) (← links)
- The anisotropic graph neural network model with multiscale and nonlinear characteristic for turbulence simulation (Q6185144) (← links)
- Peridynamic neural operators: a data-driven nonlocal constitutive model for complex material responses (Q6497150) (← links)
