arXiv:2512.06417v1 Announce Type: new 
Abstract: Fast and accurate underwater acoustic charting is crucial for downstream tasks such as environment-aware sensor placement optimization and autonomous vehicle path planning. Conventional methods rely on computationally expensive while accurate numerical solvers, which are not scalable for large-scale or real-time applications. Although deep learning-based surrogate models can accelerate these computations, they often suffer from limitations such as fixed-resolution constraints or dependence on explicit partial differential equation formulations. These issues hinder their applicability and generalization across diverse environments. We propose Hankel-FNO, a Fourier Neural Operator (FNO)-based model for efficient and accurate acoustic charting. By incorporating sound propagation knowledge and bathymetry, our method has high accuracy while maintaining high computational speed. Results demonstrate that Hankel-FNO outperforms traditional solvers in speed and surpasses data-driven alternatives in accuracy, especially in long-range predictions. Experiments show the model's adaptability to diverse environments and sound source settings with minimal fine-tuning.

تم تقديم نموذج Hankel-FNO كنهج مبتكر لرسم الخرائط الصوتية تحت الماء بسرعة ودقة، مستفيدًا من إطار عمل Fourier Neural Operator لتعزيز الكفاءة الحسابية مع الحفاظ على دقة عالية. تعالج هذه التطورات القيود المفروضة على الحلول العددية التقليدية، التي غالبًا ما تكون بطيئة جدًا للتطبيقات في الوقت الحقيقي.

Se ha introducido el modelo Hankel-FNO como un enfoque novedoso para la cartografía acústica submarina rápida y precisa, aprovechando el marco del Fourier Neural Operator para mejorar la eficiencia computacional mientras se mantiene una alta precisión. Este avance aborda las limitaciones de los solucionadores numéricos tradicionales, que a menudo son demasiado lentos para aplicaciones en tiempo real.

Le modèle Hankel-FNO a été introduit comme une approche novatrice pour le traçage acoustique sous-marin rapide et précis, s'appuyant sur le cadre du Fourier Neural Operator pour améliorer l'efficacité computationnelle tout en maintenant une grande précision. Cette avancée répond aux limitations des solveurs numériques traditionnels, souvent trop lents pour des applications en temps réel.

The Hankel-FNO model has been introduced as a novel approach for fast and accurate underwater acoustic charting, leveraging the Fourier Neural Operator framework to enhance computational efficiency while maintaining high accuracy. This advancement addresses the limitations of traditional numerical solvers, which are often too slow for real-time applications.

Hankel-FNO: Fast Underwater Acoustic Charting Via Physics-Encoded Fourier Neural Operator

arXiv:2512.07847v1 Announce Type: new 
Abstract: Benchmarking has been the cornerstone of progress in computer vision, natural language processing, and the broader deep learning domain, driving algorithmic innovation through standardized datasets and reproducible evaluation protocols. The growing availability of large-scale Computational Fluid Dynamics (CFD) datasets has opened new opportunities for applying machine learning to aerodynamic and engineering design. Yet, despite this progress, there exists no standardized benchmark for large-scale numerical simulations in engineering design. In this work, we introduce CarBench, the first comprehensive benchmark dedicated to large-scale 3D car aerodynamics, performing a large-scale evaluation of state-of-the-art models on DrivAerNet++, the largest public dataset for automotive aerodynamics, containing over 8,000 high-fidelity car simulations. We assess eleven architectures spanning neural operator methods (e.g., Fourier Neural Operator), geometric deep learning (PointNet, RegDGCNN, PointMAE, PointTransformer), transformer-based neural solvers (Transolver, Transolver++, AB-UPT), and implicit field networks (TripNet). Beyond standard interpolation tasks, we perform cross-category experiments in which transformer-based solvers trained on a single car archetype are evaluated on unseen categories. Our analysis covers predictive accuracy, physical consistency, computational efficiency, and statistical uncertainty. To accelerate progress in data-driven engineering, we open-source the benchmark framework, including training pipelines, uncertainty estimation routines based on bootstrap resampling, and pretrained model weights, establishing the first reproducible foundation for large-scale learning from high-fidelity CFD simulations, available at https://github.com/Mohamedelrefaie/CarBench.

تم تقديم CarBench كأول معيار شامل لديناميكا الهواء ثلاثية الأبعاد للسيارات على نطاق واسع، باستخدام مجموعة بيانات DrivAerNet++، التي تتضمن أكثر من 8000 محاكاة دقيقة للسيارات. يهدف هذا المعيار إلى تقييم النماذج المتطورة في مجال ديناميكا الهواء للسيارات، مما يمثل تقدمًا كبيرًا في تطبيق التعلم الآلي في تصميم الهندسة.

CarBench se ha presentado como el primer benchmark integral para la aerodinámica automotriz 3D a gran escala, utilizando el conjunto de datos DrivAerNet++, que incluye más de 8,000 simulaciones de automóviles de alta fidelidad. Este benchmark tiene como objetivo evaluar modelos de vanguardia en el campo de la aerodinámica automotriz, marcando un avance significativo en la aplicación del aprendizaje automático al diseño de ingeniería.

CarBench a été introduit comme le premier benchmark complet pour l'aérodynamique automobile 3D à grande échelle, utilisant le jeu de données DrivAerNet++, qui comprend plus de 8 000 simulations de voitures haute fidélité. Ce benchmark vise à évaluer les modèles à la pointe de la technologie dans le domaine de l'aérodynamique automobile, marquant une avancée significative dans l'application de l'apprentissage automatique à la conception d'ingénierie.

CarBench has been introduced as the first comprehensive benchmark for large-scale 3D car aerodynamics, utilizing the DrivAerNet++ dataset, which includes over 8,000 high-fidelity car simulations. This benchmark aims to evaluate state-of-the-art models in the field of automotive aerodynamics, marking a significant advancement in the application of machine learning to engineering design.

CarBench: A Comprehensive Benchmark for Neural Surrogates on High-Fidelity 3D Car Aerodynamics

arXiv:2512.07425v1 Announce Type: cross 
Abstract: Real-time monitoring of induced seismicity is crucial for mitigating operational hazards, relying on the rapid and accurate classification of microseismic events from continuous data streams. However, while many deep learning models excel at this task, their high computational requirements often limit their practical application in real-time monitoring systems. To address this limitation, a lightweight model based on the Fourier Neural Operator (FNO) is proposed for microseismic event classification, leveraging its inherent resolution-invariance and computational efficiency for waveform processing. In the STanford EArthquake Dataset (STEAD), a global and large-scale database of seismic waveforms, the FNO-based model demonstrates high effectiveness for trigger classification, with an F1 score of 95% even in the scenario of data sparsity in training. The new FNO model greatly decreases the computer power needed relative to current deep learning models without sacrificing the classification success rate measured by the F1 score. A test on a real microseismic dataset shows a classification success rate with an F1 score of 98%, outperforming many traditional deep-learning techniques. A combination of high success rate and low computational power indicates that the FNO model can serve as a methodology of choice for real-time monitoring of microseismicity for induced seismicity. The method saves computational resources and facilitates both post-processing and real-time seismic processing suitable for the implementation of traffic light systems to prevent undesired induced seismicity.

تم اقتراح نموذج خفيف الوزن يعتمد على مشغل فورييه العصبي (FNO) لتصنيف الأحداث الزلزالية الدقيقة في الوقت الحقيقي، مما يعالج المتطلبات الحاسوبية العالية للنماذج التقليدية للتعلم العميق. وقد أظهر هذا النموذج درجة F1 ملحوظة تبلغ 95% في مجموعة بيانات زلزال ستانفورد، حتى مع ندرة بيانات التدريب.

Se ha propuesto un modelo ligero basado en el Operador Neural de Fourier (FNO) para la clasificación en tiempo real de eventos micro sísmicos, abordando las altas demandas computacionales de los modelos de aprendizaje profundo tradicionales. Este modelo ha mostrado un notable puntaje F1 del 95% en el Conjunto de Datos de Terremotos de Stanford, incluso con datos de entrenamiento escasos.

Un modèle léger basé sur l'Opérateur Neural de Fourier (FNO) a été proposé pour la classification en temps réel des événements micro-sismiques, répondant aux exigences computationnelles élevées des modèles d'apprentissage profond traditionnels. Ce modèle a montré un score F1 remarquable de 95 % dans le jeu de données STanford EArthquake, même avec des données d'entraînement rares.

A lightweight model based on the Fourier Neural Operator (FNO) has been proposed for real-time classification of microseismic events, addressing the high computational demands of traditional deep learning models. This model has shown a remarkable F1 score of 95% in the STanford EArthquake Dataset, even with sparse training data.

Hankel-FNO: Fast Underwater Acoustic Charting Via Physics-Encoded Fourier Neural Operator

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