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Found 4 papers, 1 papers with code
Closing the ODE-SDE gap in score-based diffusion models through the Fokker-Planck equation
In this paper we rigorously describe the range of dynamics and approximations that arise when training score-based diffusion models, including the true SDE dynamics, the neural approximations, the various approximate particle dynamics that result, as well as their associated Fokker--Planck equations and the neural network approximations of these Fokker--Planck equations.
Your diffusion model secretly knows the dimension of the data manifold
A diffusion model approximates the score function i. e. the gradient of the log density of a noise-corrupted version of the target distribution for varying levels of corruption.
Deep surrogate accelerated delayed-acceptance HMC for Bayesian inference of spatio-temporal heat fluxes in rotating disc systems
Biot number calculations are involved in turbo-machinery design, which is safety critical and highly regulated, therefore it is important that our results have such mathematical guarantees.
A deep surrogate approach to efficient Bayesian inversion in PDE and integral equation models
The second is the development of a new, efficient deep learning-based method for Bayesian inversion applied to problems that can be described by PDEs or integral equations.
