Copertina

Multiscale Physics-Informed Neural Network for Complex Fluid Flows with Long-Range Dependencies

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Kumar, Prashant, Ranjan, Rajesh
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Fluid Dynamics Artificial Intelligence Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Multiscale Graph Neural Network for Turbulent Flow-Thermal Prediction Around a Complex-Shaped Pin-Fin
di: Raut, Riddhiman, et al.
Pubblicazione: (2025)

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

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

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

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

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes
di: Ramos, David, et al.
Pubblicazione: (2026)

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

Physics-Informed Neural Network Approaches for Sparse Data Flow Reconstruction of Unsteady Flow Around Complex Geometries
di: Malineni, Vamsi Sai Krishna, et al.
Pubblicazione: (2025)

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

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

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

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

Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach
di: Pinheiro, Nathalie C., et al.
Pubblicazione: (2026)

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

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

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

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

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

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

Flow reconstruction in time-varying geometries using graph neural networks
di: Danciu, Bogdan A., et al.
Pubblicazione: (2024)

Enhancing Graph U-Nets for Mesh-Agnostic Spatio-Temporal Flow Prediction
di: Yang, Sunwoong, et al.
Pubblicazione: (2024)

Turb-L1: Achieving Long-term Turbulence Tracing By Tackling Spectral Bias
di: Wu, Hao, et al.
Pubblicazione: (2025)

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

Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling
di: Oommen, Vivek, et al.
Pubblicazione: (2024)

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

Flow Field Reconstruction via Voronoi-Enhanced Physics-Informed Neural Networks with End-to-End Sensor Placement Optimization
di: Xiao, Renjie, et al.
Pubblicazione: (2026)

Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology
di: Xu, Qingsong, et al.
Pubblicazione: (2023)

Hard Constraint Projection in a Physics Informed Neural Network
di: Horne, Miranda J. S., et al.
Pubblicazione: (2026)

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

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

Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural Networks
di: Barwey, Shivam, et al.
Pubblicazione: (2024)

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

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

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

Multi-resolution Physics-Aware Recurrent Convolutional Neural Network for Complex Flows
di: Cheng, Xinlun, et al.
Pubblicazione: (2025)

WellPINN: Accurate Well Representation for Transient Fluid Pressure Diffusion in Subsurface Reservoirs with Physics-Informed Neural Networks
di: Walter, Linus, et al.
Pubblicazione: (2025)

Investigation of Compressor Cascade Flow Using Physics- Informed Neural Networks with Adaptive Learning Strategy
di: Li, Zhihui, et al.
Pubblicazione: (2023)

Physics-Informed Graph Neural Network Surrogates for Turbulent Nanoparticle Dispersion in Dental Clinical Environments
di: Shende, Takshak, et al.
Pubblicazione: (2026)

Solving Euler equations with Multiple Discontinuities via Separation-Transfer Physics-Informed Neural Networks
di: Wang, Chuanxing, et al.
Pubblicazione: (2025)

Explainable deep reinforcement learning reveals energy-efficient control strategies for turbulent drag reduction
di: Tonti, Federica, et al.
Pubblicazione: (2026)