Pages that link to "Item:Q2310917"
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The following pages link to A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning (Q2310917):
Displaying 23 items.
- Finite element coupled positive definite deep neural networks mechanics system for constitutive modeling of composites (Q2670357) (← links)
- A fixed point multi-scale finite volume method: application to two-phase incompressible fluid flow through highly heterogeneous porous media (Q2671311) (← links)
- Geometric learning for computational mechanics. II: Graph embedding for interpretable multiscale plasticity (Q2678490) (← links)
- Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems (Q2679283) (← links)
- Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials (Q2679297) (← links)
- Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
- <scp>Data</scp>‐physics driven reduced order homogenization (Q6071405) (← links)
- A neural kernel method for capturing multiscale high-dimensional micromorphic plasticity of materials with internal structures (Q6084451) (← links)
- Physically enhanced training for modeling rate-independent plasticity with feedforward neural networks (Q6084775) (← links)
- A structure-preserving neural differential operator with embedded Hamiltonian constraints for modeling structural dynamics (Q6109265) (← links)
- Pre-trained transformer model as a surrogate in multiscale computational homogenization framework for elastoplastic composite materials subjected to generic loading paths (Q6121691) (← links)
- Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions (Q6125484) (← links)
- Physics-based self-learning spiking neural network enhanced time-integration scheme for computing viscoplastic structural finite element response (Q6125507) (← links)
- Multiscale Modeling of Metal-Ceramic Spatially Tailored Materials via Gaussian Process Regression and Peridynamics (Q6173029) (← links)
- A framework for neural network based constitutive modelling of inelastic materials (Q6194141) (← links)
- Micromechanics-based deep-learning for composites: challenges and future perspectives (Q6540411) (← links)
- N-adaptive Ritz method: a neural network enriched partition of unity for boundary value problems (Q6566038) (← links)
- Physical informed neural network for thermo-hydral analysis of fire-loaded concrete (Q6566857) (← links)
- Data-driven physics-constrained recurrent neural networks for multiscale damage modeling of metallic alloys with process-induced porosity (Q6584871) (← links)
- Adaptive and parallel multiscale framework for modeling cohesive failure in engineering scale systems (Q6588351) (← links)
- High-order models for hydro-mechanical coupling problems in multiscale porous media (Q6592319) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
- Unsupervised machine learning classification for accelerating \(\mathrm{FE}^2\) multiscale fracture simulations (Q6641844) (← links)
