Image de couverture de livre

Geometry-Aware Physics-Informed PointNets for Modeling Flows Across Porous Structures

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Ciceri, Luigi, Mio, Corrado, Lin, Jianyi, Gianini, Gabriele
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Fluid Dynamics I.2.m
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Physics-informed KAN PointNet: Deep learning for simultaneous solutions to inverse problems in incompressible flow on numerous irregular geometries
par: Kashefi, Ali, et autres
Publié: (2025)

Physics-Informed Neural Network Approaches for Sparse Data Flow Reconstruction of Unsteady Flow Around Complex Geometries
par: Malineni, Vamsi Sai Krishna, et autres
Publié: (2025)

Visual Analysis of Displacement Processes in Porous Media using Spatio-Temporal Flow Graphs
par: Straub, Alexander, et autres
Publié: (2023)

Flow Field Reconstruction via Voronoi-Enhanced Physics-Informed Neural Networks with End-to-End Sensor Placement Optimization
par: Xiao, Renjie, et autres
Publié: (2026)

Extended Physics Informed Neural Network for Hyperbolic Two-Phase Flow in Porous Media
par: Rehman, Saif Ur, et autres
Publié: (2025)

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries
par: Rabeh, Ali, et autres
Publié: (2024)

Anisotropic Permeability Tensor Prediction from Porous Media Microstructure via Physics-Informed Progressive Transfer Learning with Hybrid CNN-Transformer
par: Nooraiepour, Mohammad
Publié: (2026)

Physics-Informed Neural Networks for Multi-Phase Flow in Porous Media Considering Dual Shocks and Interphase Solubility
par: Zhang, Jingjing, et autres
Publié: (2024)

Spectrally Informed Learning of Fluid Flows
par: Shaffer, Benjamin D., et autres
Publié: (2024)

Impact of Loss Weight and Model Complexity on Physics-Informed Neural Networks for Computational Fluid Dynamics
par: Chou, Yi En, et autres
Publié: (2025)

Sparse-Supervised Hybrid Parameterized Physics-Informed Neural Networks for Incompressible Flows Across Reynolds Numbers
par: Jangir, A., et autres
Publié: (2026)

Shock-Aware Physics-Guided Fusion-DeepONet Operator for Rarefied Micro-Nozzle Flows
par: Roohi, Ehsan, et autres
Publié: (2025)

AutoHood3D: A Multi-Modal Benchmark for Automotive Hood Design and Fluid-Structure Interaction
par: Sharma, Vansh, et autres
Publié: (2025)

Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks
par: Rabeh, Ali, et autres
Publié: (2025)

Physical Similarity of Fluid Flow in Bimodal Porous Media: Part 1 -- Basic Model and Solution Characteristics
par: Wang, Yuhe, et autres
Publié: (2024)

Improving the Robustness of Control of Chaotic Convective Flows with Domain-Informed Reinforcement Learning
par: Straat, Michiel, et autres
Publié: (2025)

Fusion-DeepONet: A Data-Efficient Neural Operator for Geometry-Dependent Hypersonic and Supersonic Flows
par: Peyvan, Ahmad, et autres
Publié: (2025)

Jacobian-Scaled K-means Clustering for Physics-Informed Segmentation of Reacting Flows
par: Barwey, Shivam, et autres
Publié: (2023)

SIMR-NO: A Spectrally-Informed Multi-Resolution Neural Operator for Turbulent Flow Super-Resolution
par: Abid, Muhammad, et autres
Publié: (2026)

Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study
par: Abbasi, Jassem, et autres
Publié: (2025)

Uncertainty-Aware Flow Field Reconstruction Using SVGP Kolmogorov-Arnold Networks
par: Ju, Y. Sungtaek
Publié: (2025)

Geometry-aware PINNs for Turbulent Flow Prediction
par: Ghosh, Shinjan, et autres
Publié: (2024)

Hard Constraint Projection in a Physics Informed Neural Network
par: Horne, Miranda J. S., et autres
Publié: (2026)

Wavelet Flow Matching for Multi-Scale Physics Emulation
par: Accarino, Gabriele, et autres
Publié: (2026)

Multiscale Physics-Informed Neural Network for Complex Fluid Flows with Long-Range Dependencies
par: Kumar, Prashant, et autres
Publié: (2026)

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

A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems
par: Menicali, Luca, et autres
Publié: (2025)

Multi-resolution Physics-Aware Recurrent Convolutional Neural Network for Complex Flows
par: Cheng, Xinlun, et autres
Publié: (2025)

Deep learning fluid flow reconstruction around arbitrary two-dimensional objects from sparse sensors using conformal mappings
par: Özbay, Ali Girayhan, et autres
Publié: (2022)

Physics-Informed Neural Networks for Transonic Flows around an Airfoil
par: Wassing, Simon, et autres
Publié: (2024)

Investigation of Compressor Cascade Flow Using Physics- Informed Neural Networks with Adaptive Learning Strategy
par: Li, Zhihui, et autres
Publié: (2023)

Droplet Simulations in Computer Graphics: Theories, Methods and Applications
par: Keshtkar, Hossein, et autres
Publié: (2024)

Digital Twin for Porous Electrodes in Redox Flow Batteries
par: Emanuel, Michael S., et autres
Publié: (2025)

Physics-Informed Chebyshev Polynomial Neural Operator for Parametric Partial Differential Equations
par: Chen, Biao, et autres
Publié: (2026)

Non-intrusive Learning of Physics-Informed Spatio-temporal Surrogate for Accelerating Design
par: Mondal, Sudeepta, et autres
Publié: (2026)

NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
par: Sharpe, Peter, et autres
Publié: (2025)

Multi-scale Dynamic Wake Modeling and Prediction of Floating Offshore Wind Turbines via Physics-Informed Neural Networks and Fourier Neural Operators
par: Dong, Guodan, et autres
Publié: (2026)

Predicting Flow Dynamics using Diffusion Models
par: Gachnang, Yannick, et autres
Publié: (2025)

A Machine Learning-Enhanced Hopf-Cole Formulation for Nonlinear Gas Flow in Porous Media
par: Maduru, V. S., et autres
Publié: (2026)

Hydrodynamically Beneficial School Configurations in Carangiform Swimmers: Insights from a Flow-Physics Informed Model
par: Zhou, Ji, et autres
Publié: (2025)