Spatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and Parameter Diffusion Guidance
- URL: http://arxiv.org/abs/2403.13850v1
- Date: Mon, 18 Mar 2024 14:57:47 GMT
- Title: Spatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and Parameter Diffusion Guidance
- Authors: Hao Wu, Fan Xu, Yifan Duan, Ziwei Niu, Weiyan Wang, Gaofeng Lu, Kun Wang, Yuxuan Liang, Yang Wang,
- Abstract summary: This paper proposes a two-stage framework named ST-PAD for capturing-temporal fluid dynamics modeling.
In the upstream stage, we design a vector reconstruction module with temporal characteristics.
In the downstream stage, a diffusion probability network involving parameters is utilized to generate high-quality future states of fluids.
- Score: 18.861739607401322
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper proposes a two-stage framework named ST-PAD for spatio-temporal fluid dynamics modeling in the field of earth sciences, aiming to achieve high-precision simulation and prediction of fluid dynamics through spatio-temporal physics awareness and parameter diffusion guidance. In the upstream stage, we design a vector quantization reconstruction module with temporal evolution characteristics, ensuring balanced and resilient parameter distribution by introducing general physical constraints. In the downstream stage, a diffusion probability network involving parameters is utilized to generate high-quality future states of fluids, while enhancing the model's generalization ability by perceiving parameters in various physical setups. Extensive experiments on multiple benchmark datasets have verified the effectiveness and robustness of the ST-PAD framework, which showcase that ST-PAD outperforms current mainstream models in fluid dynamics modeling and prediction, especially in effectively capturing local representations and maintaining significant advantages in OOD generations.
Related papers
- Trajectory Flow Matching with Applications to Clinical Time Series Modeling [77.58277281319253]
Trajectory Flow Matching (TFM) trains a Neural SDE in a simulation-free manner, bypassing backpropagation through the dynamics.
We demonstrate improved performance on three clinical time series datasets in terms of absolute performance and uncertainty prediction.
arXiv Detail & Related papers (2024-10-28T15:54:50Z) - Parametric model reduction of mean-field and stochastic systems via higher-order action matching [1.1509084774278489]
We learn models of population dynamics of physical systems that feature gradient and mean-field effects.
We show that our approach accurately predicts population dynamics over a wide range of parameters and outperforms state-of-the-art diffusion-based and flow-based modeling.
arXiv Detail & Related papers (2024-10-15T19:05:28Z) - Latent Space Energy-based Neural ODEs [73.01344439786524]
This paper introduces a novel family of deep dynamical models designed to represent continuous-time sequence data.
We train the model using maximum likelihood estimation with Markov chain Monte Carlo.
Experiments on oscillating systems, videos and real-world state sequences (MuJoCo) illustrate that ODEs with the learnable energy-based prior outperform existing counterparts.
arXiv Detail & Related papers (2024-09-05T18:14:22Z) - Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models.
Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML)
We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
arXiv Detail & Related papers (2024-07-18T15:44:23Z) - Synthetic location trajectory generation using categorical diffusion
models [50.809683239937584]
Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data.
We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
arXiv Detail & Related papers (2024-02-19T15:57:39Z) - ContPhy: Continuum Physical Concept Learning and Reasoning from Videos [86.63174804149216]
ContPhy is a novel benchmark for assessing machine physical commonsense.
We evaluated a range of AI models and found that they still struggle to achieve satisfactory performance on ContPhy.
We also introduce an oracle model (ContPRO) that marries the particle-based physical dynamic models with the recent large language models.
arXiv Detail & Related papers (2024-02-09T01:09:21Z) - Generative Modeling with Phase Stochastic Bridges [49.4474628881673]
Diffusion models (DMs) represent state-of-the-art generative models for continuous inputs.
We introduce a novel generative modeling framework grounded in textbfphase space dynamics
Our framework demonstrates the capability to generate realistic data points at an early stage of dynamics propagation.
arXiv Detail & Related papers (2023-10-11T18:38:28Z) - DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal
Forecasting [18.86526240105348]
We propose an approach for efficiently training diffusion models for probabilistic forecasting.
We train a time-conditioned interpolator and a forecaster network that mimic the forward and reverse processes of standard diffusion models.
Our approach performs competitively on probabilistic forecasting of complex dynamics in sea surface temperatures, Navier-Stokes flows, and flows spring systems.
arXiv Detail & Related papers (2023-06-03T02:46:31Z) - Physics-guided Deep Markov Models for Learning Nonlinear Dynamical
Systems with Uncertainty [6.151348127802708]
We propose a physics-guided framework, termed Physics-guided Deep Markov Model (PgDMM)
The proposed framework takes advantage of the expressive power of deep learning, while retaining the driving physics of the dynamical system.
arXiv Detail & Related papers (2021-10-16T16:35:12Z) - Disentangled Generative Models for Robust Prediction of System Dynamics [2.6424064030995957]
In this work, we treat the domain parameters of dynamical systems as factors of variation of the data generating process.
By leveraging ideas from supervised disentanglement and causal factorization, we aim to separate the domain parameters from the dynamics in the latent space of generative models.
Results indicate that disentangled VAEs adapt better to domain parameters spaces that were not present in the training data.
arXiv Detail & Related papers (2021-08-26T09:58:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.