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Found 4 papers, 2 papers with code
Inpainting Computational Fluid Dynamics with Deep Learning
Fluid data completion is a research problem with high potential benefit for both experimental and computational fluid dynamics.
Latent Neural PDE Solver: a reduced-order modelling framework for partial differential equations
Neural networks have shown promising potential in accelerating the numerical simulation of systems governed by partial differential equations (PDEs).
Scalable Transformer for PDE Surrogate Modeling
These sub-functions are then evaluated and used to compute the instance-based kernel with an axial factorized scheme.
A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction
Machine learning models are gaining increasing popularity in the domain of fluid dynamics for their potential to accelerate the production of high-fidelity computational fluid dynamics data.
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