Related papers: Unfolding Time: Generative Modeling for Turbulent Flows in 4D

Unfolding Time: Generative Modeling for Turbulent Flows in 4D

URL: http://arxiv.org/abs/2406.11390v1
Date: Mon, 17 Jun 2024 10:21:01 GMT
Title: Unfolding Time: Generative Modeling for Turbulent Flows in 4D
Authors: Abdullah Saydemir, Marten Lienen, Stephan Günnemann,
Abstract summary: This work introduces a 4D generative diffusion model and a physics-informed guidance technique that enables the generation of realistic sequences of flow states. Our findings indicate that the proposed method can successfully sample entire subsequences from the turbulent manifold. This advancement opens doors for the application of generative modeling in analyzing the temporal evolution of turbulent flows.
Score: 49.843505326598596
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: A recent study in turbulent flow simulation demonstrated the potential of generative diffusion models for fast 3D surrogate modeling. This approach eliminates the need for specifying initial states or performing lengthy simulations, significantly accelerating the process. While adept at sampling individual frames from the learned manifold of turbulent flow states, the previous model lacks the capability to generate sequences, hindering analysis of dynamic phenomena. This work addresses this limitation by introducing a 4D generative diffusion model and a physics-informed guidance technique that enables the generation of realistic sequences of flow states. Our findings indicate that the proposed method can successfully sample entire subsequences from the turbulent manifold, even though generalizing from individual frames to sequences remains a challenging task. This advancement opens doors for the application of generative modeling in analyzing the temporal evolution of turbulent flows, providing valuable insights into their complex dynamics.

Related papers

Energy-Based Diffusion Language Models for Text Generation [126.23425882687195]
Energy-based Diffusion Language Model (EDLM) is an energy-based model operating at the full sequence level for each diffusion step. Our framework offers a 1.3$times$ sampling speedup over existing diffusion models.
arXiv Detail & Related papers (2024-10-28T17:25:56Z)
Geometric Trajectory Diffusion Models [58.853975433383326]
Generative models have shown great promise in generating 3D geometric systems. Existing approaches only operate on static structures, neglecting the fact that physical systems are always dynamic in nature. We propose geometric trajectory diffusion models (GeoTDM), the first diffusion model for modeling the temporal distribution of 3D geometric trajectories.
arXiv Detail & Related papers (2024-10-16T20:36:41Z)
Text-to-Image Rectified Flow as Plug-and-Play Priors [52.586838532560755]
Rectified flow is a novel class of generative models that enforces a linear progression from the source to the target distribution. We show that rectified flow approaches surpass in terms of generation quality and efficiency, requiring fewer inference steps. Our method also displays competitive performance in image inversion and editing.
arXiv Detail & Related papers (2024-06-05T14:02:31Z)
Physics-enhanced Neural Operator for Simulating Turbulent Transport [9.923888452768919]
This paper presents a physics-enhanced neural operator (PENO) that incorporates physical knowledge of partial differential equations (PDEs) to accurately model flow dynamics. The proposed method is evaluated through its performance on two distinct sets of 3D turbulent flow data.
arXiv Detail & Related papers (2024-05-31T20:05:17Z)
4Diffusion: Multi-view Video Diffusion Model for 4D Generation [55.82208863521353]
Current 4D generation methods have achieved noteworthy efficacy with the aid of advanced diffusion generative models. We propose a novel 4D generation pipeline, namely 4Diffusion, aimed at generating spatial-temporally consistent 4D content from a monocular video.
arXiv Detail & Related papers (2024-05-31T08:18:39Z)
Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation [13.278744447861289]
We introduce a novel generative framework grounded in probabilistic diffusion models for turbulence generation. A notable feature of our approach is the proposed method for long-span flow sequence generation, which is based on autoregressive-based conditional sampling. We showcase the versatile turbulence generation capability of our framework through a suite of numerical experiments.
arXiv Detail & Related papers (2023-11-14T04:08:14Z)
Spontaneous Symmetry Breaking in Generative Diffusion Models [6.4322891559626125]
Generative diffusion models have recently emerged as a leading approach for generating high-dimensional data. We show that the dynamics of these models exhibit a spontaneous symmetry breaking that divides the generative dynamics into two distinct phases. We propose a new way to understand the generative dynamics of diffusion models that has the potential to bring about higher performance and less biased fast-samplers.
arXiv Detail & Related papers (2023-05-31T09:36:34Z)
From Zero to Turbulence: Generative Modeling for 3D Flow Simulation [45.626346087828765]
We propose to approach turbulent flow simulation as a generative task directly learning the manifold of all possible turbulent flow states without relying on any initial flow state. Our generative model captures the distribution of turbulent flows caused by unseen objects and generates high-quality, realistic samples for downstream applications.
arXiv Detail & Related papers (2023-05-29T18:20:28Z)
Automating Turbulence Modeling by Multi-Agent Reinforcement Learning [4.784658158364452]
We introduce multi-agent reinforcement learning as an automated discovery tool of turbulence models. We demonstrate the potential of this approach on Large Eddy Simulations of homogeneous and isotropic turbulence.
arXiv Detail & Related papers (2020-05-18T18:45:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.