Predicting Flow Dynamics using Diffusion Models
In this work, we aimed to replicate and extend the results presented in the DiffFluid paper[1]. The DiffFluid model showed that diffusion models combined with Transformers are capable of predicting fluid dynamics. It uses a denoising diffusion probabilistic model (DDPM) framework to tackle Navier-Stokes and Darcy flow equations. Our goal was to validate the reproducibility of the methods in the DiffFluid paper while testing its viability for other simulation types, particularly the Lattice Boltzmann method. Despite our computational limitations and time constraints, this work provides evidence of the flexibility and potential of the model as a general-purpose solver for fluid dynamics. Our results show both the potential and challenges of applying diffusion models to complex fluid dynamics problems. This work highlights the opportunities for future research in optimizing the computational efficiency and scaling such models in broader domains.
Code (0)
등록된 구현이 없습니다.
Tasks
Computational EfficiencySimilar Papers 제목 키워드 기반
Bidirectional Autoregressive Latent Diffusion for Forward and Inverse Magnetohydrodynamics
This work presents a new bidirectional autoregressive latent diffusion approach for predicting the evolution of multiple fields (mass density, pressure, velocity, and magnetic field components) for magnetohydrodynamics. …
Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks
Physical systems with complex unsteady dynamics, such as fluid flows, are often poorly represented by a single mean solution. For many practical applications, it is crucial to access the full distribution of possible sta…
Computational EfficiencyGuiding diffusion models to reconstruct flow fields from sparse data
The reconstruction of unsteady flow fields from limited measurements is a challenging and crucial task for many engineering applications. Machine learning models are gaining popularity for solving this problem due to the…
A Denoising Diffusion Model for Fluid Field Prediction
We propose a novel denoising diffusion generative model for predicting nonlinear fluid fields named FluidDiff. By performing a diffusion process, the model is able to learn a complex representation of the high-dimensiona…
DenoisingmodelPredictionFoilDiff: A Hybrid Transformer Backbone for Diffusion-based Modelling of 2D Airfoil Flow Fields
The accurate prediction of flow fields around airfoils is crucial for aerodynamic design and optimisation. Computational Fluid Dynamics (CFD) models are effective but computationally expensive, thus inspiring the develop…