Cover Image

Fluids You Can Trust: Property-Preserving Operator Learning for Incompressible Flows

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Sharma, Ramansh, Lowery, Matthew, Owhadi, Houman, Shankar, Varun
Format:	Preprint
Published:	2026
Subjects:	Fluid Dynamics Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Project and Generate: Divergence-Free Neural Operators for Incompressible Flows
by: Li, Xigui, et al.
Published: (2026)

Ensemble and Mixture-of-Experts DeepONets For Operator Learning
by: Sharma, Ramansh, et al.
Published: (2024)

Preconditioned FEM-based Neural Networks for Solving Incompressible Fluid Flows and Related Inverse Problems
by: Griese, Franziska, et al.
Published: (2024)

Spectrally Informed Learning of Fluid Flows
by: Shaffer, Benjamin D., 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)

Fusion-DeepONet: A Data-Efficient Neural Operator for Geometry-Dependent Hypersonic and Supersonic Flows
by: Peyvan, Ahmad, et al.
Published: (2025)

Generalizable data-driven turbulence closure modeling on unstructured grids with differentiable physics
by: Kim, Hojin, et al.
Published: (2023)

Differentiable Turbulence: Closure as a partial differential equation constrained optimization
by: Shankar, Varun, et al.
Published: (2023)

A Variational Computational-based Framework for Unsteady Incompressible Flows
by: Sababha, H., et al.
Published: (2024)

Neural Physics: Using AI Libraries to Develop Physics-Based Solvers for Incompressible Computational Fluid Dynamics
by: Chen, Boyang, 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)

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

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

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

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

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

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

BLISSNet: Deep Operator Learning for Fast and Accurate Flow Reconstruction from Sparse Sensor Measurements
by: Veremchuk, Maksym, et al.
Published: (2026)

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

MENO: MeanFlow-Enhanced Neural Operators for Dynamical Systems
by: Yang, Tianyue, et al.
Published: (2026)

Efficient Computation of Large-Scale Statistical Solutions to Incompressible Fluid Flows
by: Rohner, Tobias, et al.
Published: (2024)

OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics
by: Zhang, Rui, et al.
Published: (2025)

An Adaptive Machine Learning Framework for Fluid Flow in Dual-Network Porous Media
by: Maduri, V. S., et al.
Published: (2026)

Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks
by: Rabeh, Ali, et al.
Published: (2025)

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

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

SIMR-NO: A Spectrally-Informed Multi-Resolution Neural Operator for Turbulent Flow Super-Resolution
by: Abid, Muhammad, et al.
Published: (2026)

Shock-Aware Physics-Guided Fusion-DeepONet Operator for Rarefied Micro-Nozzle Flows
by: Roohi, Ehsan, et al.
Published: (2025)

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

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

Structure-preserving Randomized Neural Networks for Incompressible Magnetohydrodynamics Equations
by: Li, Yunlong, et al.
Published: (2026)

AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics
by: Ashton, Neil, et al.
Published: (2024)

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

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)

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

Characteristic Bending in Incompressible Flows
by: Blomquist, Matthew, et al.
Published: (2025)

Component-Based Machine Learning for Indoor Flow and Temperature Fields Prediction Latent Feature Aggregation and Flow Interaction
by: Wang, Shaofan, et al.
Published: (2025)

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

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