Cover Image

OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Rui, Meng, Qi, Wan, Han, Liu, Yang, Ma, Zhi-Ming, Sun, Hao
Format:	Preprint
Published:	2025
Subjects:	Fluid Dynamics Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

LLM4Fluid: Large Language Models as Generalizable Neural Solvers for Fluid Dynamics
by: Xiao, Qisong, et al.
Published: (2026)

Impact of Loss Weight and Model Complexity on Physics-Informed Neural Networks for Computational Fluid Dynamics
by: Chou, Yi En, et al.
Published: (2025)

LinguaFluid: Language Guided Fluid Control via Semantic Rewards in Reinforcement Learning
by: Liang, Aoming, et al.
Published: (2025)

Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics
by: Toshev, Artur P., et al.
Published: (2024)

Koopman Autoencoders with Continuous-Time Latent Dynamics for Fluid Dynamics Forecasting
by: Grozavescu, Rares, et al.
Published: (2026)

DeepLag: Discovering Deep Lagrangian Dynamics for Intuitive Fluid Prediction
by: Ma, Qilong, et al.
Published: (2024)

Spatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and Parameter Diffusion Guidance
by: Wu, Hao, et al.
Published: (2024)

A Finite Element-Inspired Hypergraph Neural Network: Application to Fluid Dynamics Simulations
by: Gao, Rui, et al.
Published: (2022)

Inpainting Computational Fluid Dynamics with Deep Learning
by: Shu, Dule, et al.
Published: (2024)

Model-Agnostic AI Framework with Explicit Time Integration for Long-Term Fluid Dynamics Prediction
by: Yang, Sunwoong, et al.
Published: (2024)

DiffFluid: Plain Diffusion Models are Effective Predictors of Flow Dynamics
by: Luo, Dongyu, et al.
Published: (2024)

VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction
by: Cao, Yadi, et al.
Published: (2024)

Transported Memory Networks accelerating Computational Fluid Dynamics
by: Schulz, Matthias, et al.
Published: (2025)

FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation
by: Wang, Haixin, et al.
Published: (2025)

PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics
by: Liu, Yuxuan, et al.
Published: (2024)

DSO: Dual-Scale Neural Operators for Stable Long-term Fluid Dynamics Forecasting
by: Dong, Huanshuo, et al.
Published: (2026)

Spectrally Informed Learning of Fluid Flows
by: Shaffer, Benjamin D., et al.
Published: (2024)

Neural Kinematic Bases for Fluids
by: Liu, Yibo, et al.
Published: (2025)

Combining Machine Learning with Computational Fluid Dynamics using OpenFOAM and SmartSim
by: Maric, Tomislav, et al.
Published: (2024)

Optimal Parallelization Strategies for Active Flow Control in Deep Reinforcement Learning-Based Computational Fluid Dynamics
by: Jia, Wang, et al.
Published: (2024)

A Misleading Gallery of Fluid Motion by Generative Artificial Intelligence
by: Kashefi, Ali
Published: (2024)

Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time
by: Xu, Jingxuan, et al.
Published: (2025)

Neural Physics: Using AI Libraries to Develop Physics-Based Solvers for Incompressible Computational Fluid Dynamics
by: Chen, Boyang, et al.
Published: (2024)

HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics
by: Ashton, Neil, et al.
Published: (2026)

CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics
by: Luo, Yining, et al.
Published: (2023)

The compressible Neural Particle Method for Simulating Compressible Viscous Fluid Flows
by: Shibukawa, Masato, et al.
Published: (2025)

Coupling Machine Learning Local Predictions with a Computational Fluid Dynamics Solver to Accelerate Transient Buoyant Plume Simulations
by: Caron, Clément, et al.
Published: (2024)

HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
by: Lagemann, Christian, et al.
Published: (2025)

Fluids You Can Trust: Property-Preserving Operator Learning for Incompressible Flows
by: Sharma, Ramansh, et al.
Published: (2026)

LAViG-FLOW: Latent Autoregressive Video Generation for Fluid Flow Simulations
by: De Pellegrini, Vittoria, et al.
Published: (2026)

An ALE-Consistent Graph Neural Operator-Transformer Framework for Fluid-Structure Interaction
by: Zhao, Shihang, et al.
Published: (2026)

Targeted Digital Twin via Flow Map Learning and Its Application to Fluid Dynamics
by: Chen, Qifan, et al.
Published: (2025)

FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization
by: Ling, Tianheng, et al.
Published: (2024)

Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements
by: Dang, Hiep Vo, et al.
Published: (2024)

Data-Driven Reduced-Complexity Modeling of Fluid Flows: A Community Challenge
by: Schmidt, Oliver T., et al.
Published: (2026)

Multiscale Physics-Informed Neural Network for Complex Fluid Flows with Long-Range Dependencies
by: Kumar, Prashant, et al.
Published: (2026)

A review on Deep Reinforcement Learning for Fluid Mechanics
by: Garnier, Paul, et al.
Published: (2019)

Graph-Based Learning of Free Surface Dynamics in Generalized Newtonian Fluids using Smoothed Particle Hydrodynamics
by: Kim, Hyo-Jin, et al.
Published: (2025)

Contactless Precision Steering of Particles in a Fluid inside a Cube with Rotating Walls
by: Amoudruz, Lucas, et al.
Published: (2025)

Physical Fidelity Reconstruction via Improved Consistency-Distilled Flow Matching for Dynamical Systems
by: Ma, Sicheng, et al.
Published: (2026)