Imagen de Portada

A Comprehensive Evaluation of Graph Neural Networks and Physics Informed Learning for Surrogate Modelling of Finite Element Analysis

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autor principal:	Singh, Nayan Kumar
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Physics-Informed Neural Network Surrogate Models for River Stage Prediction
por: Zoch, Maximilian, et al.
Publicado: (2025)

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

Relaxation-Informed Training of Neural Network Surrogate Models
por: Tsay, Calvin
Publicado: (2026)

Neural Network Surrogate and Projected Gradient Descent for Fast and Reliable Finite Element Model Calibration: a Case Study on an Intervertebral Disc
por: Atad, Matan, et al.
Publicado: (2024)

A Multimodal, Multitask System for Generating E Commerce Text Listings from Images
por: Singh, Nayan Kumar
Publicado: (2025)

Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems
por: Liu, Tong, et al.
Publicado: (2022)

Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
por: Cestero, Julen, et al.
Publicado: (2025)

A Framework for Strategic Discovery of Credible Neural Network Surrogate Models under Uncertainty
por: Singh, Pratyush Kumar, et al.
Publicado: (2024)

A Comprehensively Adaptive Architectural Optimization-Ingrained Quantum Neural Network Model for Cloud Workloads Prediction
por: Kumar, Jitendra, et al.
Publicado: (2025)

Benchmarking Physics-Informed Neural Networks and Boundary Elements Methods for Wave Scattering
por: Rincón-Cardeno, Oscar, et al.
Publicado: (2025)

Generalizable and Fast Surrogates: Model Predictive Control of Articulated Soft Robots using Physics-Informed Neural Networks
por: Habich, Tim-Lukas, et al.
Publicado: (2025)

Statistical Learning Analysis of Physics-Informed Neural Networks
por: Barajas-Solano, David A.
Publicado: (2026)

SOLIS: Physics-Informed Learning of Interpretable Neural Surrogates for Nonlinear Systems
por: Mansur, Murat Furkan, et al.
Publicado: (2026)

FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models
por: Raut, Riddhiman, et al.
Publicado: (2025)

Direct Interval Propagation Methods using Neural-Network Surrogates for Uncertainty Quantification in Physical Systems Surrogate Model
por: Faza, Ghifari Adam, et al.
Publicado: (2026)

Physics-Informed Machine Learning Regulated by Finite Element Analysis for Simulation Acceleration of Melt Pool Dynamics in Laser Powder Bed Fusion
por: Sharma, R., et al.
Publicado: (2025)

Model Based and Physics Informed Deep Learning Neural Network Structures
por: Mohammad-Djafari, Ali, et al.
Publicado: (2024)

Hybrid Quantum Classical Surrogate for Real Time Inverse Finite Element Modeling in Digital Twins
por: Alavi, Azadeh, et al.
Publicado: (2025)

A Comprehensive Survey on Graph Summarization with Graph Neural Networks
por: Shabani, Nasrin, et al.
Publicado: (2023)

Interpretable A-posteriori Error Indication for Graph Neural Network Surrogate Models
por: Barwey, Shivam, et al.
Publicado: (2023)

Physics-Informed Graph Neural Networks to Reconstruct Local Fields Considering Finite Strain Hyperelasticity
por: Garban, Manuel Ricardo Guevara, et al.
Publicado: (2025)

Benchmarking Convolutional Neural Network and Graph Neural Network based Surrogate Models on a Real-World Car External Aerodynamics Dataset
por: Jacob, Sam Jacob, et al.
Publicado: (2025)

Learning PDE Solvers with Physics and Data: A Unifying View of Physics-Informed Neural Networks and Neural Operators
por: Dai, Yilong, et al.
Publicado: (2026)

Element-wise Multiplication Based Deeper Physics-Informed Neural Networks
por: Jiang, Feilong, et al.
Publicado: (2024)

Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts
por: Rodriguez, Arturo, et al.
Publicado: (2024)

Physics-Informed Graph Neural Networks for Water Distribution Systems
por: Ashraf, Inaam, et al.
Publicado: (2024)

Graph Neural Networks for Emulation of Finite-Element Ice Dynamics in Greenland and Antarctic Ice Sheets
por: Koo, Younghyun, et al.
Publicado: (2024)

Graph Network Surrogate Model for Subsurface Flow Optimization
por: Tang, Haoyu, et al.
Publicado: (2023)

Evaluation of Neural Surrogates for Physical Modelling Synthesis of Nonlinear Elastic Plates
por: Martin, Carlos De La Vega, et al.
Publicado: (2025)

Finite-PINN: A Physics-Informed Neural Network with Finite Geometric Encoding for Solid Mechanics
por: Li, Haolin, et al.
Publicado: (2024)

Physics-Informed Mixture Models and Surrogate Models for Precision Additive Manufacturing
por: Basterrech, Sebastian, et al.
Publicado: (2025)

Neural-Initialized Newton: Accelerating Nonlinear Finite Elements via Operator Learning
por: Taghikhani, Kianoosh, et al.
Publicado: (2025)

FIMP: Foundation Model-Informed Message Passing for Graph Neural Networks
por: Rizvi, Syed Asad, et al.
Publicado: (2022)

A Hybrid Surrogate for Electric Vehicle Parameter Estimation and Power Consumption via Physics-Informed Neural Operators
por: Lim, Hansol, et al.
Publicado: (2025)

Domain-Decomposed Graph Neural Network Surrogate Modeling for Ice Sheets
por: Propp, Adrienne M., et al.
Publicado: (2025)

Conformalized Physics-Informed Neural Networks
por: Podina, Lena, et al.
Publicado: (2024)

Scalable Quantum Error Mitigation with Physically Informed Graph Neural Networks
por: Wang, Huaxin, et al.
Publicado: (2026)

Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement
por: Jin, Hongwei, et al.
Publicado: (2024)

Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems
por: Jahin, Md Abrar, et al.
Publicado: (2025)

Physics-Informed Neural Networks for Vessel Trajectory Prediction: Learning Time-Discretized Kinematic Dynamics via Finite Differences
por: Alam, Md Mahbub, et al.
Publicado: (2025)