OmniFluids: Unified Physics Pre-trained Modeling of Fluid Dynamics

• URL: http://arxiv.org/abs/2506.10862v1
• Date: Thu, 12 Jun 2025 16:23:02 GMT
• Title: OmniFluids: Unified Physics Pre-trained Modeling of Fluid Dynamics
• Authors: Rui Zhang, Qi Meng, Han Wan, Yang Liu, Zhi-Ming Ma, Hao Sun,
• Abstract summary: We introduce OmniFluids, a unified physics pre-trained operator learning framework.<n>It integrates physics-only pre-training, coarse-grid operator distillation, and few-shot fine-tuning.<n>It significantly outperforms state-of-the-art AI-driven methods in flow field reconstruction and turbulence statistics accuracy.
• Score: 25.066485418709114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-fidelity and efficient simulation of fluid dynamics drive progress in various scientific and engineering applications. Traditional computational fluid dynamics methods offer strong interpretability and guaranteed convergence, but rely on fine spatial and temporal meshes, incurring prohibitive computational costs. Physics-informed neural networks (PINNs) and neural operators aim to accelerate PDE solvers using deep learning techniques. However, PINNs require extensive retraining and careful tuning, and purely data-driven operators demand large labeled datasets. Hybrid physics-aware methods embed numerical discretizations into network architectures or loss functions, but achieve marginal speed gains and become unstable when balancing coarse priors against high-fidelity measurements. To this end, we introduce OmniFluids, a unified physics pre-trained operator learning framework that integrates physics-only pre-training, coarse-grid operator distillation, and few-shot fine-tuning, which enables fast inference and accurate prediction under limited or zero data supervision. For architectural design, the key components of OmniFluids include a mixture of operators, a multi-frame decoder, and factorized Fourier layers, which enable efficient and scalable modeling of diverse physical tasks while maintaining seamless integration with physics-based supervision. Across a broad range of two- and three-dimensional benchmarks, OmniFluids significantly outperforms state-of-the-art AI-driven methods in flow field reconstruction and turbulence statistics accuracy, delivering 10-100x speedups compared to classical solvers, and accurately recovers unknown physical parameters from sparse, noisy data. This work establishes a new paradigm for efficient and generalizable surrogate modeling in complex fluid systems under limited data availability.

Related papers

• PMNO: A novel physics guided multi-step neural operator predictor for partial differential equations [23.04840527974364]
  We propose a novel physics guided multi-step neural operator (PMNO) architecture to address challenges in long-horizon prediction of complex physical systems.<n>The PMNO framework replaces the single-step input with multi-step historical data in the forward pass and introduces an implicit time-stepping scheme during backpropagation.<n>We demonstrate the superior predictive performance of PMNO predictor across a diverse range of physical systems.
  arXiv  Detail & Related papers  (2025-06-02T12:33:50Z)
• Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time [57.30651532625017]
  We present a novel hybrid method that integrates numerical simulation, neural physics, and generative control.<n>Our system demonstrates robust performance across diverse 2D/3D scenarios, material types, and obstacle interactions.<n>We promise to release both models and data upon acceptance.
  arXiv  Detail & Related papers  (2025-05-25T01:27:18Z)
• High-fidelity Multiphysics Modelling for Rapid Predictions Using Physics-informed Parallel Neural Operator [17.85837423448985]
  Modelling complex multiphysics systems governed by nonlinear and strongly coupled partial differential equations (PDEs) is a cornerstone in computational science and engineering.<n>We propose a novel paradigm, physics-informed parallel neural operator (PIPNO), a scalable and unsupervised learning framework.<n>PIPNO efficiently captures nonlinear operator mappings across diverse physics, including geotechnical engineering, material science, electromagnetism, quantum mechanics, and fluid dynamics.
  arXiv  Detail & Related papers  (2025-02-26T20:29:41Z)
• Fine-Tuning Hybrid Physics-Informed Neural Networks for Vehicle Dynamics Model Estimation [2.432448600920501]
  This paper introduces the Fine-Tuning Hybrid Dynamics (FTHD) method, which integrates supervised and unsupervised Physics-Informed Neural Networks (PINNs) FTHD fine-tunes a pre-trained Deep Dynamics Model (DDM) using a smaller training dataset, delivering superior performance compared to state-of-the-art methods. An Extended Kalman Filter (EKF) is embedded within FTHD to effectively manage noisy real-world data, ensuring accurate denoising. Results demonstrate that the hybrid approach significantly improves parameter estimation accuracy, even with reduced data, and outperforms existing models.
  arXiv  Detail & Related papers  (2024-09-29T10:33:07Z)
• Liquid Fourier Latent Dynamics Networks for fast GPU-based numerical simulations in computational cardiology [0.0]
  We propose an extension of Latent Dynamics Networks (LDNets) to create parameterized space-time surrogate models for multiscale and multiphysics sets of highly nonlinear differential equations on complex geometries. LFLDNets employ a neurologically-inspired, sparse liquid neural network for temporal dynamics, relaxing the requirement of a numerical solver for time advancement and leading to superior performance in terms of parameters, accuracy, efficiency and learned trajectories.
  arXiv  Detail & Related papers  (2024-08-19T09:14:25Z)
• Multi-fidelity physics constrained neural networks for dynamical systems [16.6396704642848]
  We propose the Multi-Scale Physics-Constrained Neural Network (MSPCNN) MSPCNN offers a novel methodology for incorporating data with different levels of fidelity into a unified latent space. Unlike conventional methods, MSPCNN also manages to employ multi-fidelity data to train the predictive model.
  arXiv  Detail & Related papers  (2024-02-03T05:05:26Z)
• Neural Operators for Accelerating Scientific Simulations and Design [85.89660065887956]
  An AI framework, known as Neural Operators, presents a principled framework for learning mappings between functions defined on continuous domains. Neural Operators can augment or even replace existing simulators in many applications, such as computational fluid dynamics, weather forecasting, and material modeling.
  arXiv  Detail & Related papers  (2023-09-27T00:12:07Z)
• Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes [37.69303106863453]
  We present a hybrid quantum physics-informed neural network that simulates laminar fluid flows in 3D Y-shaped mixers. Our approach combines the expressive power of a quantum model with the flexibility of a physics-informed neural network, resulting in a 21% higher accuracy compared to a purely classical neural network.
  arXiv  Detail & Related papers  (2023-04-21T20:49:29Z)
• NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition [67.46012350241969]
  This paper proposes a general acceleration methodology called NeuralStagger. It decomposing the original learning tasks into several coarser-resolution subtasks. We demonstrate the successful application of NeuralStagger on 2D and 3D fluid dynamics simulations.
  arXiv  Detail & Related papers  (2023-02-20T19:36:52Z)
• On Fast Simulation of Dynamical System with Neural Vector Enhanced Numerical Solver [59.13397937903832]
  We introduce a deep learning-based corrector called Neural Vector (NeurVec) NeurVec can compensate for integration errors and enable larger time step sizes in simulations. Our experiments on a variety of complex dynamical system benchmarks demonstrate that NeurVec exhibits remarkable generalization capability.
  arXiv  Detail & Related papers  (2022-08-07T09:02:18Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)

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.