Imagen de Portada

PiRD: Physics-informed Residual Diffusion for Flow Field Reconstruction

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Shan, Siming, Wang, Pengkai, Chen, Song, Liu, Jiaxu, Xu, Chao, Cai, Shengze
Formato:	Preprint
Publicado:	2024
Materias:	Fluid Dynamics Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

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

Distributed physics-informed neural networks via domain decomposition for fast flow reconstruction
por: Qian, Yixiao, et al.
Publicado: (2026)

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

LatentFlow: Cross-Frequency Experimental Flow Reconstruction from Sparse Pressure via Latent Mapping
por: Liu, Junle, et al.
Publicado: (2025)

Spatially-Aware Diffusion Models with Cross-Attention for Global Field Reconstruction with Sparse Observations
por: Zhuang, Yilin, et al.
Publicado: (2024)

Diff-SPORT: Diffusion-based Sensor Placement Optimization and Reconstruction of Turbulent flows in urban environments
por: Vishwasrao, Abhijeet, et al.
Publicado: (2025)

Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction
por: Oommen, Vivek, et al.
Publicado: (2025)

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

Modeling Continuous Spatial-temporal Dynamics of Turbulent Flow with Test-time Refinement
por: Chen, Shengyu, et al.
Publicado: (2024)

Reconstructing 3D Flow from 2D Data with Diffusion Transformer
por: Lei, Fan
Publicado: (2024)

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

Operator Learning for Reconstructing Flow Fields from Sparse Measurements: an Energy Transformer Approach
por: Zhang, Qian, et al.
Publicado: (2025)

An Efficient Wavelet-based Physics Informed Residual Neural Networks for Flow Field Reconstruction with Extremely Sparse Data
por: Barman, Biswanath, et al.
Publicado: (2026)

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

Physics-informed Neural-operator Predictive Control for Drag Reduction in Turbulent Flows
por: Zhao, Zelin, et al.
Publicado: (2025)

Navigation in a Three-Dimensional Urban Flow using Deep Reinforcement Learning
por: Tonti, Federica, et al.
Publicado: (2025)

A Multimodal Vision Transformer-based Modeling Framework for Prediction of Fluid Flows in Energy Systems
por: Yalamanchi, Kiran, et al.
Publicado: (2026)

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

MetaOpenFOAM: an LLM-based multi-agent framework for CFD
por: Chen, Yuxuan, et al.
Publicado: (2024)

Surrogate Model for Heat Transfer Prediction in Impinging Jet Arrays using Dynamic Inlet/Outlet and Flow Rate Control
por: Vaillant, Mikael, et al.
Publicado: (2025)

Data-Free PINNs for Compressible Flows: Mitigating Spectral Bias and Gradient Pathologies via Mach-Guided Scaling and Hybrid Convolutions
por: Yano, Ryosuke
Publicado: (2026)

OptMetaOpenFOAM: Large Language Model Driven Chain of Thought for Sensitivity Analysis and Parameter Optimization based on CFD
por: Chen, Yuxuan, et al.
Publicado: (2025)

TrajectoryFlowNet: Lagrangian-Eulerian learning of flow field and trajectories
por: Wan, Jingdi, et al.
Publicado: (2025)

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

Transformer-based Neural Operators for 3D Wind Field Prediction over Complex Mountainous Terrain
por: Zhang, Yujia, et al.
Publicado: (2026)

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

Physics-Informed Neural Networks for Enhanced Interface Preservation in Lattice Boltzmann Multiphase Simulations
por: Li, Yue, et al.
Publicado: (2025)

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

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

A Framework of Data Assimilation for Wind Flow Fields by Physics-informed Neural Networks
por: Yan, Chang, et al.
Publicado: (2024)

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

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

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

Deep learning accelerated solutions of incompressible Navier-Stokes equations on non-uniform Cartesian grids
por: Bai, Heming, et al.
Publicado: (2026)

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

A neural operator framework for data-driven discovery of stability and receptivity in physical systems
por: Wang, Chengyun, et al.
Publicado: (2026)

Learnable-Differentiable Finite Volume Solver for Accelerated Simulation of Flows
por: Yan, Mengtao, et al.
Publicado: (2025)

High-Fidelity Reconstruction of Charge Boundary Layers and Sharp Interfaces in Electro-Thermal-Convective Flows via Residual-Attention PINNs
por: Zhou, Baitong, et al.
Publicado: (2026)

OGF: An Online Gradient Flow Method for Optimizing the Statistical Steady-State Time Averages of Unsteady Turbulent Flows
por: Hickling, Tom, et al.
Publicado: (2025)

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