The final version of our NeurIPS paper merging physics simulations into the diffusion modeling process (SMDP) is on arXiv now: https://arxiv.org/pdf/2301.10250.pdf
Maybe even more importantly, the SMDP source code is online how at: https://github.com/tum-pbs/SMDP , let us know how it works for you!
Here’s an overview of the algorithm:
Here’s a preview of one of the examples diffusing a very simply decaying “physics” function:
Full paper abstract: Our works proposes a novel approach to solve inverse problems involving the temporal evolution of physics systems by leveraging the idea of score matching. The system’s current state is moved backward in time step by step by combining an approximate inverse physics simulator and a learned correction function. A central insight of our work is that training the learned correction with a single-step loss is equivalent to a score matching objective, while recursively predicting longer parts of the trajectory during training relates to maximum likelihood training of a corresponding probability flow. In the paper, we highlight the advantages of our algorithm compared to standard denoising score matching and implicit score matching, as well as fully learned baselines for a wide range of inverse physics problems. The resulting inverse solver has excellent accuracy and temporal stability and, in contrast to other learned inverse solvers, allows for sampling the posterior of the solutions.
