Bayesian conditional diffusion models for versatile spatiotemporal turbulence generation
Turbulent flows, characterized by their chaotic and stochastic nature, have historically
presented formidable challenges to predictive computational modeling. Traditional eddy …
presented formidable challenges to predictive computational modeling. Traditional eddy …
Diffusion models as probabilistic neural operators for recovering unobserved states of dynamical systems
This paper explores the efficacy of diffusion-based generative models as neural operators
for partial differential equations (PDEs). Neural operators are neural networks that learn a …
for partial differential equations (PDEs). Neural operators are neural networks that learn a …
Generative downscaling of PDE solvers with physics-guided diffusion models
Solving partial differential equations (PDEs) on fine spatio-temporal scales for high-fidelity
solutions is critical for numerous scientific breakthroughs. Yet, this process can be …
solutions is critical for numerous scientific breakthroughs. Yet, this process can be …
Manifold-Guided Lyapunov Control with Diffusion Models
This paper presents a novel approach to generating stabilizing controllers for a large class
of dynamical systems using diffusion models. The core objective is to develop stabilizing …
of dynamical systems using diffusion models. The core objective is to develop stabilizing …
Denoising Diffusion Restoration Tackles Forward and Inverse Problems for the Laplace Operator
Diffusion models have emerged as a promising class of generative models that map noisy
inputs to realistic images. More recently, they have been employed to generate solutions to …
inputs to realistic images. More recently, they have been employed to generate solutions to …