Here’s also a talk summarizing our recent work on diffusion models for probabilistic Neural solvers: https://youtu.be/xaWxERImy0g
It covers the whole range: from steady state cases, over time-dependent surrogate models, all the way to integrating differentiable simulations into learning score functions. And here are the three corresponding papers:
- Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models , https://arxiv.org/pdf/2312.05320
- Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation , https://arxiv.org/pdf/2309.01745
- Solving Inverse Physics Problems with Score Matching , https://arxiv.org/pdf/2301.10250.pdf
