Fluids You Can Trust: Property-Preserving Operator Learning for Incompressible Flows
physics.flu-dyn, cs.LG
Submitted: 2026-02-17
Updated: 2026-10-07
Project page: https://su2code.github.io/tutorials/Inc_Species_Transport_Composition_Dependent_Model
Terminology
Sources
- KROM: Kernelized Reduced Order Modeling
- The Interpolation Theory of Radial Basis Functions
- Lattice algorithms for multivariate approximation in periodic spaces with general weight parameters
- Adaptive Inducing Points Selection For Gaussian Processes
- GNOT: A General Neural Operator Transformer for Operator Learning
- A physics-encoded Fourier neural operator approach for surrogate modeling of divergence-free stress fields in solids
- Fourier Neural Operator for Parametric Partial Differential Equations
- Neural Operator: Graph Kernel Network for Partial Differential Equations
- Geometry-Informed Neural Operator Transformer
- Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning
- Reduced Basis Methods: Success, Limitations and Future Challenges
- Transolver: A Fast Transformer Solver for PDEs on General Geometries
- Floating-Body Hydrodynamic Neural Networks
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