Pages that link to "Item:Q6184289"
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The following pages link to Discontinuity computing using physics-informed neural networks (Q6184289):
Displaying 10 items.
- A conservative and positivity-preserving method for solving anisotropic diffusion equations with deep learning (Q6537080) (← links)
- Extremization to fine tune physics informed neural networks for solving boundary value problems (Q6591803) (← links)
- TGPT-PINN: nonlinear model reduction with transformed GPT-PINNs (Q6595863) (← links)
- Solving high-dimensional parametric engineering problems for inviscid flow around airfoils based on physics-informed neural networks (Q6615001) (← links)
- PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs (Q6643563) (← links)
- A neural particle method with interface tracking and adaptive particle refinement for free surface flows (Q6646467) (← links)
- Improving weak PINNs for hyperbolic conservation laws: dual norm computation, boundary conditions and systems (Q6658817) (← links)
- Approximating families of sharp solutions to Fisher's equation with physics-informed neural networks (Q6660246) (← links)
- Least-square finite difference-based physics-informed neural network for steady incompressible flows (Q6663359) (← links)
- Unsupervised neural-network solvers for multi-material Riemann problems (Q6671955) (← links)
