arXiv:2510.25683v1 Announce Type: new 
Abstract: Graph Neural Networks (GNNs) have recently been explored as surrogate models for numerical simulations. While their applications in computational fluid dynamics have been investigated, little attention has been given to structural problems, especially for dynamic cases. To address this gap, we introduce the Graph Network-based Structural Simulator (GNSS), a GNN framework for surrogate modeling of dynamic structural problems.
  GNSS follows the encode-process-decode paradigm typical of GNN-based machine learning models, and its design makes it particularly suited for dynamic simulations thanks to three key features: (i) expressing node kinematics in node-fixed local frames, which avoids catastrophic cancellation in finite-difference velocities; (ii) employing a sign-aware regression loss, which reduces phase errors in long rollouts; and (iii) using a wavelength-informed connectivity radius, which optimizes graph construction.
  We evaluate GNSS on a case study involving a beam excited by a 50kHz Hanning-modulated pulse. The results show that GNSS accurately reproduces the physics of the problem over hundreds of timesteps and generalizes to unseen loading conditions, where existing GNNs fail to converge or deliver meaningful predictions.
  Compared with explicit finite element baselines, GNSS achieves substantial inference speedups while preserving spatial and temporal fidelity. These findings demonstrate that locality-preserving GNNs with physics-consistent update rules are a competitive alternative for dynamic, wave-dominated structural simulations.

تقديم جهاز المحاكاة الهيكلية القائم على الشبكات البيانية (GNSS) يمثل تقدمًا كبيرًا في تطبيق الشبكات العصبية البيانية (GNN) لمشاكل الهيكل الديناميكي. على الرغم من استخدام GNN في الديناميكا الهوائية الحاسوبية، إلا أن إمكانياتها في الديناميكا الهيكلية تم تجاهلها إلى حد كبير. يهدف هذا الإطار الجديد إلى سد هذه الفجوة، مما يوفر أداة واعدة لمحاكاة عددية أكثر كفاءة ودقة في الهندسة. يمكن أن يؤدي تطوير GNSS إلى تحسين عمليات التصميم وتقييمات السلامة في تطبيقات هيكلية متنوعة.

La introducción del Simulador Estructural Basado en Redes de Grafos (GNSS) marca un avance significativo en la aplicación de Redes Neuronales de Grafos (GNN) para problemas estructurales dinámicos. Aunque las GNN se han utilizado en dinámica de fluidos computacional, su potencial en dinámica estructural ha sido en gran medida pasado por alto. Este nuevo marco busca llenar ese vacío, proporcionando una herramienta prometedora para simulaciones numéricas más eficientes y precisas en ingeniería. El desarrollo de GNSS podría llevar a procesos de diseño mejorados y evaluaciones de seguridad en diversas aplicaciones estructurales.

L'introduction du simulateur structurel basé sur les réseaux de graphes (GNSS) représente une avancée significative dans l'application des réseaux de neurones graphiques (GNN) aux problèmes structurels dynamiques. Bien que les GNN aient été utilisés en dynamique des fluides computationnelle, leur potentiel en dynamique structurelle a été largement négligé. Ce nouveau cadre vise à combler cette lacune, offrant un outil prometteur pour des simulations numériques plus efficaces et précises en ingénierie. Le développement du GNSS pourrait conduire à des processus de conception améliorés et à des évaluations de sécurité dans diverses applications structurelles.

The introduction of the Graph Network-based Structural Simulator (GNSS) marks a significant advancement in the application of Graph Neural Networks (GNNs) for dynamic structural problems. While GNNs have been utilized in computational fluid dynamics, their potential in structural dynamics has been largely overlooked. This new framework aims to fill that gap, providing a promising tool for more efficient and accurate numerical simulations in engineering. The development of GNSS could lead to improved design processes and safety assessments in various structural applications.

Graph Network-based Structural Simulator: Graph Neural Networks for Structural Dynamics

arXiv:2503.22252v2 Announce Type: replace-cross 
Abstract: Graph neural networks, recently introduced into the field of fluid flow surrogate modeling, have been successfully applied to model the temporal evolution of various fluid flow systems. Existing applications, however, are mostly restricted to cases where the domain is time-invariant. The present work extends the application of graph neural network-based modeling to fluid flow around structures rotating with respect to a certain axis. Specifically, we propose to apply a graph neural network-based surrogate model with part of the mesh/graph co-rotating with the structure and part of the mesh/graph static. A single layer of interface cells are constructed at the interface between the two parts and are allowed to distort and adapt, which helps in circumventing the difficulty of interpolating information encoded by the neural network at every graph neural network layer. Dedicated reconstruction and re-projection schemes are designed to counter the error caused by the distortion and connectivity change of the interface cells. The effectiveness of our proposed framework is examined on two test cases: (i) fluid flow around a 2D oscillating airfoil, and (ii) fluid flow past a 3D rotating cube. Our results show that the model achieves stable rollout predictions over hundreds or even a thousand time steps. We further demonstrate that one could enforce accurate, error-bounded prediction results by incorporating the measurements from sparse pressure sensors. In addition to the accurate flow field predictions, the lift and drag force predictions closely match with the computational fluid dynamics calculations, highlighting the potential of the framework for modeling fluid flow around rotating structures, and paving the path towards a graph neural network-based surrogate model for more complex scenarios like flow around marine propellers.

تقدم دراسة جديدة شبكة عصبية هيبرغرافية قابلة للتكيف مع الشبكات، مصممة لنمذجة تدفق السوائل غير المستقر حول الهياكل المتذبذبة والدورانية، مما يوسع تطبيق الشبكات العصبية الرسومية في ديناميات السوائل. يسمح هذا النهج المبتكر لجزء من الشبكة بالدوران مع الهيكل بينما يحتفظ بجزء ثابت، مما يسهل تحسين استيفاء المعلومات عبر طبقات الشبكة.

Un nuevo estudio presenta una red neuronal hipergráfica adaptativa a mallas, diseñada para modelar el flujo de fluidos no estacionario alrededor de estructuras oscilantes y rotativas, ampliando la aplicación de redes neuronales gráficas en la dinámica de fluidos. Este enfoque innovador permite que parte de la malla co-rotee con la estructura mientras mantiene una porción estática, facilitando una mejor interpolación de información a través de las capas de la red.

Une nouvelle étude présente un réseau de neurones hypergraphe adaptatif à maillage, conçu pour modéliser l'écoulement de fluide instable autour de structures oscillantes et tournantes, étendant l'application des réseaux de neurones graphiques en dynamique des fluides. Cette approche innovante permet à une partie du maillage de co-rotationner avec la structure tout en maintenant une portion statique, facilitant ainsi une meilleure interpolation des informations à travers les couches du réseau.

A new study introduces a mesh-adaptive hypergraph neural network designed to model unsteady fluid flow around oscillating and rotating structures, extending the application of graph neural networks in fluid dynamics. This innovative approach allows part of the mesh to co-rotate with the structure while maintaining a static portion, facilitating better information interpolation across the network layers.

A Mesh-Adaptive Hypergraph Neural Network for Unsteady Flow Around Oscillating and Rotating Structures

arXiv:2511.11048v2 Announce Type: replace-cross 
Abstract: 4D flow magnetic resonance imaging (MRI) is a reliable, non-invasive approach for estimating blood flow velocities, vital for cardiovascular diagnostics. Unlike conventional MRI focused on anatomical structures, 4D flow MRI requires high spatiotemporal resolution for early detection of critical conditions such as stenosis or aneurysms. However, achieving such resolution typically results in prolonged scan times, creating a trade-off between acquisition speed and prediction accuracy. Recent studies have leveraged physics-informed neural networks (PINNs) for super-resolution of MRI data, but their practical applicability is limited as the prohibitively slow training process must be performed for each patient. To overcome this limitation, we propose PINGS-X, a novel framework modeling high-resolution flow velocities using axes-aligned spatiotemporal Gaussian representations. Inspired by the effectiveness of 3D Gaussian splatting (3DGS) in novel view synthesis, PINGS-X extends this concept through several non-trivial novel innovations: (i) normalized Gaussian splatting with a formal convergence guarantee, (ii) axes-aligned Gaussians that simplify training for high-dimensional data while preserving accuracy and the convergence guarantee, and (iii) a Gaussian merging procedure to prevent degenerate solutions and boost computational efficiency. Experimental results on computational fluid dynamics (CFD) and real 4D flow MRI datasets demonstrate that PINGS-X substantially reduces training time while achieving superior super-resolution accuracy. Our code and datasets are available at https://github.com/SpatialAILab/PINGS-X.

تم تقديم إطار عمل جديد يسمى PINGS-X لتحسين كفاءة الدقة الفائقة في التصوير بالرنين المغناطيسي بتدفق 4D، وهو أمر حاسم لتقدير سرعات تدفق الدم بدقة في تشخيص القلب والأوعية الدموية. تستخدم هذه الطريقة تمثيلات غاوسية موحدة مستندة إلى الفيزياء مع محاذاة المحاور لمعالجة التحديات المتعلقة بأوقات المسح المطولة والتوازن بين سرعة الاكتساب ودقة التنبؤ.

Se ha introducido un nuevo marco llamado PINGS-X para mejorar la eficiencia de la super-resolución en la IRM 4D, que es crucial para la estimación precisa de las velocidades del flujo sanguíneo en el diagnóstico cardiovascular. Este enfoque utiliza un splatting gaussiano normalizado informado por la física con alineación de ejes para abordar los desafíos de los tiempos de escaneo prolongados y el compromiso entre la velocidad de adquisición y la precisión de las predicciones.

Un nouveau cadre nommé PINGS-X a été introduit pour améliorer l'efficacité de la super-résolution en IRM 4D, qui est cruciale pour une estimation précise des vitesses de flux sanguin dans le diagnostic cardiovasculaire. Cette approche utilise un splatting gaussien normalisé informé par la physique avec alignement des axes pour relever les défis des temps de scan prolongés et du compromis entre la vitesse d'acquisition et la précision des prédictions.

A novel framework named PINGS-X has been introduced to enhance the efficiency of super-resolution in 4D flow MRI, which is crucial for accurate blood flow velocity estimation in cardiovascular diagnostics. This approach utilizes physics-informed normalized Gaussian splatting with axes alignment to address the challenges of prolonged scan times and the trade-off between acquisition speed and prediction accuracy.

Graph Network-based Structural Simulator: Graph Neural Networks for Structural Dynamics

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