Pages that link to "Item:Q2145138"
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The following pages link to Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training (Q2145138):
Displaying 24 items.
- SciANN (Q54044) (← links)
- Machine learning for accelerating macroscopic parameters prediction for poroelasticity problem in stochastic media (Q2226818) (← links)
- Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
- Deep learning-accelerated computational framework based on physics informed neural network for the solution of linear elasticity (Q6053463) (← links)
- Some optimally convergent algorithms for decoupling the computation of Biot's model (Q6057161) (← links)
- Mathematical effects of linear visco-elasticity in quasi-static Biot models (Q6112505) (← links)
- A novel physics-informed deep learning strategy with local time-updating discrete scheme for multi-dimensional forward and inverse consolidation problems (Q6121797) (← links)
- Physics-based self-learning spiking neural network enhanced time-integration scheme for computing viscoplastic structural finite element response (Q6125507) (← links)
- Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading (Q6164292) (← links)
- Physical information neural networks for 2D and 3D nonlinear Biot model and simulation on the pressure of brain (Q6173340) (← links)
- Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks (Q6173359) (← links)
- A neural network-based enrichment of reproducing kernel approximation for modeling brittle fracture (Q6185157) (← links)
- Physics-informed graph neural network emulation of soft-tissue mechanics (Q6194151) (← links)
- I-FENN with temporal convolutional networks: expediting the load-history analysis of non-local gradient damage propagation (Q6497179) (← links)
- Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models (Q6537067) (← links)
- Mesh reduction methods for thermoelasticity of laminated composite structures: study on the B-spline based state space finite element method and physics-informed neural networks (Q6540214) (← links)
- SPI-MIONet for surrogate modeling in phase-field hydraulic fracturing (Q6557819) (← links)
- Physical informed neural network for thermo-hydral analysis of fire-loaded concrete (Q6566857) (← links)
- Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains (Q6569914) (← links)
- Variational temporal convolutional networks for I-FENN thermoelasticity (Q6588274) (← links)
- Learning solutions of thermodynamics-based nonlinear constitutive material models using physics-informed neural networks (Q6604129) (← links)
- Interpretable physics-encoded finite element network to handle concentration features and multi-material heterogeneity in hyperelasticity (Q6609781) (← links)
- On physics-informed neural networks training for coupled hydro-poromechanical problems (Q6615008) (← links)
- I-FENN for thermoelasticity based on physics-informed temporal convolutional network (PI-TCN) (Q6661937) (← links)
