arXiv:2510.23810v1 Announce Type: cross 
Abstract: The predictive accuracy of operator learning frameworks depends on the quality and quantity of available training data (input-output function pairs), often requiring substantial amounts of high-fidelity data, which can be challenging to obtain in some real-world engineering applications. These datasets may be unevenly discretized from one realization to another, with the grid resolution varying across samples. In this study, we introduce a physics-informed operator learning approach by extending the Resolution Independent Neural Operator (RINO) framework to a fully data-free setup, addressing both challenges simultaneously. Here, the arbitrarily (but sufficiently finely) discretized input functions are projected onto a latent embedding space (i.e., a vector space of finite dimensions), using pre-trained basis functions. The operator associated with the underlying partial differential equations (PDEs) is then approximated by a simple multi-layer perceptron (MLP), which takes as input a latent code along with spatiotemporal coordinates to produce the solution in the physical space. The PDEs are enforced via a finite difference solver in the physical space. The validation and performance of the proposed method are benchmarked on several numerical examples with multi-resolution data, where input functions are sampled at varying resolutions, including both coarse and fine discretizations.

تقدم دراسة جديدة مشغلًا عصبيًا متعدد الدقة مستندًا إلى الفيزياء يهدف إلى تحسين الدقة التنبؤية لأطر تعلم المشغلين. هذا مهم لأنه يعالج التحديات المتعلقة بالحصول على بيانات تدريب عالية الدقة في تطبيقات الهندسة الواقعية، حيث يمكن أن تكون البيانات متباينة في التوزيع. من خلال تحسين الطريقة التي تتعلم بها هذه الأطر من البيانات المتاحة، يمكن أن تؤدي هذه الأبحاث إلى توقعات أكثر موثوقية في مجالات الهندسة المختلفة.

Un nuevo estudio presenta un operador neural multi-resolución informado por la física, con el objetivo de mejorar la precisión predictiva de los marcos de aprendizaje de operadores. Esto es significativo porque aborda los desafíos de obtener datos de entrenamiento de alta fidelidad en aplicaciones de ingeniería del mundo real, donde los datos pueden estar discretizados de manera desigual. Al mejorar la forma en que estos marcos aprenden de los datos disponibles, la investigación podría llevar a predicciones más confiables en varios campos de la ingeniería.

Une nouvelle étude présente un opérateur neural multi-résolution informé par la physique, visant à améliorer la précision prédictive des cadres d'apprentissage des opérateurs. Cela est important car cela répond aux défis d'obtention de données d'entraînement de haute fidélité dans des applications d'ingénierie réelles, où les données peuvent être discrétisées de manière inégale. En améliorant la façon dont ces cadres apprennent à partir des données disponibles, la recherche pourrait conduire à des prédictions plus fiables dans divers domaines de l'ingénierie.

A new study introduces a physics-informed multi-resolution neural operator aimed at improving the predictive accuracy of operator learning frameworks. This is significant because it addresses the challenges of obtaining high-fidelity training data in real-world engineering applications, where data can be unevenly discretized. By enhancing the way these frameworks learn from available data, the research could lead to more reliable predictions in various engineering fields.

A Physics-informed Multi-resolution Neural Operator

arXiv:2509.19580v5 Announce Type: replace 
Abstract: Cutting-edge Artificial Intelligence (AI) techniques keep reshaping our view of the world. For example, Large Language Models (LLMs) based applications such as ChatGPT have shown the capability of generating human-like conversation on extensive topics. Due to the impressive performance on a variety of language-related tasks (e.g., open-domain question answering, translation, and document summarization), one can envision the far-reaching impacts that can be brought by the LLMs with broader real-world applications (e.g., customer service, education and accessibility, and scientific discovery). Inspired by their success, this paper will offer an overview of state-of-the-art LLMs and their integration into a wide range of academic disciplines, including: (1) arts, letters, and law (e.g., history, philosophy, political science, arts and architecture, law), (2) economics and business (e.g., finance, economics, accounting, marketing), and (3) science and engineering (e.g., mathematics, physics and mechanical engineering, chemistry and chemical engineering, life sciences and bioengineering, earth sciences and civil engineering, computer science and electrical engineering). Integrating humanity and technology, in this paper, we will explore how LLMs are shaping research and practice in these fields, while also discussing key limitations, open challenges, and future directions in the era of generative AI. The review of how LLMs are engaged across disciplines-along with key observations and insights-can help researchers and practitioners interested in exploiting LLMs to advance their works in diverse real-world applications.

تستعرض مراجعة حديثة بعنوان 'LLMs4All' الإمكانيات التحويلية لنماذج اللغة الكبيرة (LLMs) عبر مجموعة متنوعة من التخصصات الأكاديمية، بما في ذلك الفنون والاقتصاد والقانون. يبرز المقال قدرات نماذج اللغة الكبيرة، مثل ChatGPT، في توليد محادثات تشبه المحادثات البشرية وأداء مهام لغوية معقدة، مما يشير إلى تطبيقات حقيقية مهمة في مجالات مثل التعليم والاكتشاف العلمي.

Una reciente revisión titulada 'LLMs4All' destaca el potencial transformador de los Modelos de Lenguaje Grande (LLMs) en diversas disciplinas académicas, incluyendo las artes, la economía y el derecho. El documento enfatiza las capacidades de los LLMs, como ChatGPT, para generar conversaciones similares a las humanas y realizar tareas complejas relacionadas con el lenguaje, sugiriendo aplicaciones significativas en el mundo real en campos como la educación y el descubrimiento científico.

Une récente revue intitulée 'LLMs4All' met en lumière le potentiel transformateur des grands modèles de langage (LLMs) dans diverses disciplines académiques, y compris les arts, l'économie et le droit. L'article souligne les capacités des LLMs, tels que ChatGPT, à générer des conversations semblables à celles des humains et à réaliser des tâches linguistiques complexes, suggérant des applications réelles significatives dans des domaines comme l'éducation et la découverte scientifique.

A recent review titled 'LLMs4All' highlights the transformative potential of Large Language Models (LLMs) across various academic disciplines, including arts, economics, and law. The paper emphasizes the capabilities of LLMs, such as ChatGPT, in generating human-like conversations and performing complex language-related tasks, suggesting significant real-world applications in fields like education and scientific discovery.

LLMs4All: A Review of Large Language Models Across Academic Disciplines

arXiv:2511.18533v1 Announce Type: new 
Abstract: Accurate segmentation of individual teeth from panoramic radiographs remains a challenging task due to anatomical variations, irregular tooth shapes, and overlapping structures. These complexities often limit the performance of conventional deep learning models. To address this, we propose DE-KAN, a novel Dual Encoder Kolmogorov Arnold Network, which enhances feature representation and segmentation precision. The framework employs a ResNet-18 encoder for augmented inputs and a customized CNN encoder for original inputs, enabling the complementary extraction of global and local spatial features. These features are fused through KAN-based bottleneck layers, incorporating nonlinear learnable activation functions derived from the Kolmogorov Arnold representation theorem to improve learning capacity and interpretability. Extensive experiments on two benchmark dental X-ray datasets demonstrate that DE-KAN outperforms state-of-the-art segmentation models, achieving mIoU of 94.5%, Dice coefficient of 97.1%, accuracy of 98.91%, and recall of 97.36%, representing up to +4.7% improvement in Dice compared to existing methods.

تم تقديم إطار عمل جديد يسمى DE-KAN، يستخدم شبكة كولموغوروف أرنولد ذات الترميز المزدوج لتحسين دقة تقسيم الأسنان ثنائية الأبعاد من الأشعة السينية البانورامية. تتناول هذه الطريقة التحديات مثل الاختلافات التشريحية والهياكل المتداخلة التي أعاقت تاريخياً أداء التقسيم. يجمع الإطار بين مشفر ResNet-18 للمدخلات المعززة ومشفر CNN مخصص للمدخلات الأصلية، مما يسمح بتحسين استخراج الميزات.

Se ha introducido un nuevo marco llamado DE-KAN, que utiliza una Red Kolmogorov Arnold de Doble Codificador para mejorar la precisión de la segmentación de dientes en 2D a partir de radiografías panorámicas. Este enfoque aborda desafíos como las variaciones anatómicas y las estructuras superpuestas que históricamente han obstaculizado el rendimiento de la segmentación. El marco combina un codificador ResNet-18 para entradas aumentadas y un codificador CNN personalizado para entradas originales, permitiendo una mejor extracción de características.

Un nouveau cadre nommé DE-KAN a été introduit, utilisant un réseau Kolmogorov Arnold à double encodeur pour améliorer la précision de la segmentation des dents en 2D à partir de radiographies panoramiques. Cette approche répond à des défis tels que les variations anatomiques et les structures qui se chevauchent, qui ont historiquement entravé la performance de la segmentation. Le cadre combine un encodeur ResNet-18 pour les entrées augmentées et un encodeur CNN personnalisé pour les entrées originales, permettant une meilleure extraction des caractéristiques.

A new framework named DE-KAN has been introduced, utilizing a Dual Encoder Kolmogorov Arnold Network to enhance the accuracy of 2D teeth segmentation from panoramic radiographs. This approach addresses challenges such as anatomical variations and overlapping structures that have historically hindered segmentation performance. The framework combines a ResNet-18 encoder for augmented inputs and a customized CNN encoder for original inputs, allowing for improved feature extraction.

A Physics-informed Multi-resolution Neural Operator

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