OCL-PDE: A Generative Framework for PDE Inverse Problems with Observation-Complementary Latents
cs.LG
Submitted: 2026-10-05
Updated: 2026-10-05
Terminology
Sources
- A unified physics-informed generative operator framework for general inverse problems
- Gaussian Error Linear Units (GELUs)
- Fourier Neural Operator for Parametric Partial Differential Equations
- Flow Matching for Generative Modeling
- Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
- Generative downscaling of PDE solvers with physics-guided diffusion models
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