:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Li, Yue, Zhang, Lihong
Format:	Preprint
Published:	2025
Subjects:	Fluid Dynamics Artificial Intelligence
Online Access:	https://arxiv.org/abs/2504.10539
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

LBM-GNN: Graph Neural Network Enhanced Lattice Boltzmann Method
by: Li, Yue
Published: (2025)

Cross-Field Interface-Aware Neural Operators for Multiphase Flow Simulation
by: Wang, Zhenzhong, et al.
Published: (2025)

Higher-order Tuning of Interface Physics in Multiphase Lattice Boltzmann
by: Lulli, Matteo, et al.
Published: (2025)

PT-PINNs: A Parametric Engineering Turbulence Solver based on Physics-Informed Neural Networks
by: Jiang, Liang, et al.
Published: (2025)

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

PINNfluence: Influence Functions for Physics-Informed Neural Networks
by: Naujoks, Jonas R., et al.
Published: (2024)

NeuralFluid: Neural Fluidic System Design and Control with Differentiable Simulation
by: Li, Yifei, et al.
Published: (2024)

Physics-Constrained Neural Closure for Lattice Boltzmann Large-Eddy Simulation
by: Khan, Muhammad Idrees, et al.
Published: (2026)

Simulating Three-dimensional Turbulence with Physics-informed Neural Networks
by: Wang, Sifan, et al.
Published: (2025)

Physics-informed Shadowgraph Network: An End-to-end Density Field Reconstruction Method
by: Wang, Xutun, et al.
Published: (2024)

A Robust Lattice Boltzmann Method for Interface-Bound Transport of a Passive Scalar: Application to Surfactant-Laden Multiphase Flows
by: Schupbach, William, et al.
Published: (2025)

PEST: Physics-Enhanced Swin Transformer for 3D Turbulence Simulation
by: Dai, Yilong, et al.
Published: (2026)

The Principle of Minimum Pressure Gradient: An Alternative Basis for Physics-Informed Learning of Incompressible Fluid Mechanics
by: Alhussein, Hussam, et al.
Published: (2024)

AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents
by: Somasekharan, Nithin, et al.
Published: (2026)

Physics-Informed Neural Networks for Weakly Compressible Flows Using Galerkin-Boltzmann Formulation
by: Aygun, Atakan, et al.
Published: (2024)

LLM Agent for Fire Dynamics Simulations
by: Xu, Leidong, et al.
Published: (2024)

Hybrid Fourier Neural Operator-Lattice Boltzmann Method
by: Junk, Alexandra, et al.
Published: (2026)

accLB: A High-Performance Lattice Boltzmann Code for Multiphase Turbulence on Multi-Gpu Architectures
by: Lauricella, Marco, et al.
Published: (2025)

Fine-tuning a Large Language Model for Automating Computational Fluid Dynamics Simulations
by: Dong, Zhehao, et al.
Published: (2025)

Data-Efficient Deep Operator Network for Unsteady Flow: A Multi-Fidelity Approach with Physics-Guided Subsampling
by: Yang, Sunwoong, et al.
Published: (2025)

Deep reinforcement learning for tracking a moving target in jellyfish-like swimming
by: Chen, Yihao, et al.
Published: (2024)

An Edge-based Interface Tracking (EBIT) Method for Multiphase-flows Simulation with Surface Tension
by: Pan, Jieyun, et al.
Published: (2023)

Tensor Network Lattice Boltzmann Method for Data-Compressed Fluid Simulations
by: Gross, Lukas, et al.
Published: (2025)

Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks
by: Lino, Mario, et al.
Published: (2025)

Exploring the Applicability of the Lattice-Boltzmann Method for Two-Dimensional Turbulence Simulation
by: Dapena-García, Raquel, et al.
Published: (2025)

A Three-dimensional Edge-Based Interface Tracking (EBIT) Method for Multiphase-flow Simulations
by: Pan, Jieyun, et al.
Published: (2025)

PAINT: Parallel-in-time Neural Twins for Dynamical System Reconstruction
by: Radler, Andreas, et al.
Published: (2025)

Pi-fusion: Physics-informed diffusion model for learning fluid dynamics
by: Qiu, Jing, et al.
Published: (2024)

Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence
by: Mukhopadhyay, Payel, et al.
Published: (2026)

Full-field prediction for engineering-scale three-dimensional aircraft with multigrid-hierarchical learning
by: Liu, Yunfei, et al.
Published: (2026)

Enhancing Arterial Blood Flow Simulations through Physics-Informed Neural Networks
by: Bhargava, Shivam, 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)

Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators
by: Alkin, Benedikt, et al.
Published: (2024)

Lattice Boltzmann Boundary Conditions for Flow, Convection-Diffusion and MHD Simulations
by: Li, Jun, et al.
Published: (2025)

PiRD: Physics-informed Residual Diffusion for Flow Field Reconstruction
by: Shan, Siming, et al.
Published: (2024)

Neural-Network-based Viscosity Closure for Non-Newtonian Multiphase Flows
by: Murugaiyan, Suresh, et al.
Published: (2026)

Lattice Boltzmann methodology for unconfined flows
by: Sahiti, Vemu, et al.
Published: (2026)

Investigation of Numerical Diffusion in Aerodynamic Flow Simulations with Physics Informed Neural Networks
by: Warey, Alok, et al.
Published: (2021)

Modeling Dynamic Gas-Liquid Interfaces in Underwater Explosions Using Interval-Constrained Physics-Informed Neural Networks
by: Xing, Fulin, et al.
Published: (2025)

Physics-guided surrogate learning enables zero-shot control of turbulent wings
by: Wang, Yuning, et al.
Published: (2026)