Pages that link to "Item:Q2021855"
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The following pages link to Discretizationnet: a machine-learning based solver for Navier-Stokes equations using finite volume discretization (Q2021855):
Displaying 26 items.
- PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
- Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems (Q2072742) (← links)
- CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method (Q2142144) (← links)
- Learning finite element convergence with the multi-fidelity graph neural network (Q2145122) (← links)
- An exploratory study on machine learning to couple numerical solutions of partial differential equations (Q2656809) (← links)
- Structure preservation for the deep neural network multigrid solver (Q2672194) (← links)
- A physics-informed convolutional neural network for the simulation and prediction of two-phase Darcy flows in heterogeneous porous media (Q2681146) (← links)
- QBoost for regression problems: solving partial differential equations (Q2687371) (← links)
- VPVnet: A Velocity-Pressure-Vorticity Neural Network Method for the Stokes’ Equations under Reduced Regularity (Q5065192) (← links)
- MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs (Q5106291) (← links)
- Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning (Q5162375) (← links)
- Machine Learning Surrogate Modeling for Meshless Methods: Leveraging Universal Approximation (Q6048309) (← links)
- A nonlocal energy‐informed neural network for isotropic elastic solids with cracks under thermomechanical loads (Q6082574) (← links)
- Solving seepage equation using physics-informed residual network without labeled data (Q6120152) (← links)
- A conservative hybrid deep learning method for Maxwell-Ampère-Nernst-Planck equations (Q6126572) (← links)
- Physical informed memory networks for solving PDEs: implementation and applications (Q6497869) (← links)
- Efficient simulation of two-dimensional time-fractional Navier-Stokes equations using RBF-FD approach (Q6545776) (← links)
- Neural-integrated meshfree (NIM) method: a differentiable programming-based hybrid solver for computational mechanics (Q6557785) (← links)
- Enhancing physics informed neural networks for solving Navier-Stokes equations (Q6574171) (← links)
- Machine learning predictive model for dynamic response of rising bubbles impacting on a horizontal wall (Q6588314) (← links)
- Cubic and quartic hyperbolic B-splines comparison for coupled Navier Stokes equation via differential quadrature method -- a statistical aspect (Q6590251) (← links)
- Advanced physics-informed neural networks for numerical approximation of the coupled Schrödinger-KdV equation (Q6590978) (← links)
- Transfer learning enhanced nonlocal energy-informed neural network for quasi-static fracture in rock-like materials (Q6595896) (← links)
- Deep learning in computational mechanics: a review (Q6604128) (← links)
- f-PICNN: a physics-informed convolutional neural network for partial differential equations with space-time domain (Q6614990) (← links)
- Differentiability in unrolled training of neural physics simulators on transient dynamics (Q6663245) (← links)
