arXiv:2512.00483v2 Announce Type: replace-cross 
Abstract: Data-driven modeling of building thermal dynamics is emerging as an increasingly important field of research for large-scale intelligent building control. However, research in data-driven modeling using machine learning (ML) techniques requires massive amounts of thermal building data, which is not easily available. Neither empirical public datasets nor existing data generators meet the needs of ML research in terms of data quality and quantity. Moreover, existing data generation approaches typically require expert knowledge in building simulation. To fill this gap, we present a thermal building data generation framework which we call BuilDa. BuilDa is designed to produce synthetic data of adequate quality and quantity for ML research. The framework does not require profound building simulation knowledge to generate large volumes of data. BuilDa uses a single-zone Modelica model that is exported as a Functional Mock-up Unit (FMU) and simulated in Python. We demonstrate BuilDa by generating data and utilizing it for a transfer learning study involving the fine-tuning of 486 data-driven models.

تم تقديم إطار عمل جديد يسمى BuilDa لتوليد بيانات حرارية للمباني على نطاق واسع، مما يلبي الحاجة المتزايدة لمجموعات بيانات عالية الجودة في أبحاث التعلم الآلي المتعلقة بالديناميات الحرارية للمباني. يبسط هذا الإطار عملية توليد البيانات، مما يلغي الحاجة إلى خبرة واسعة في محاكاة المباني.

Se ha presentado un nuevo marco llamado BuilDa para generar datos térmicos de edificios a gran escala, abordando la creciente necesidad de conjuntos de datos de alta calidad en la investigación de aprendizaje automático relacionada con la dinámica térmica de los edificios. Este marco simplifica el proceso de generación de datos, eliminando la necesidad de una amplia experiencia en simulación de edificios.

Un nouveau cadre nommé BuilDa a été introduit pour générer des données thermiques de bâtiments à grande échelle, répondant au besoin croissant de jeux de données de haute qualité dans la recherche en apprentissage automatique liée à la dynamique thermique des bâtiments. Ce cadre simplifie le processus de génération de données, éliminant le besoin d'une expertise approfondie en simulation de bâtiments.

A new framework named BuilDa has been introduced for generating large-scale thermal building data, addressing the growing need for high-quality datasets in machine learning research related to building thermal dynamics. This framework simplifies the data generation process, eliminating the need for extensive expertise in building simulation.

A Highly Configurable Framework for Large-Scale Thermal Building Data Generation to drive Machine Learning Research

arXiv:2503.02259v2 Announce Type: replace 
Abstract: Gaussian Processes (GPs) are flexible, nonparametric Bayesian models widely used for regression and classification because of their ability to capture complex data patterns and quantify predictive uncertainty. However, the O(n^3) computational cost of kernel matrix operations poses a major obstacle to applying GPs at scale. HiGP is a high-performance Python package designed to overcome these scalability limitations through advanced numerical linear algebra and hierarchical kernel representations. It integrates H^2 matrices to achieve near-linear complexity in both storage and computation for spatial datasets, supports on-the-fly kernel evaluation to avoid explicit storage in large-scale problems, and incorporates a robust Adaptive Factorized Nystr\"om (AFN) preconditioner that accelerates convergence of iterative solvers across a broad range of kernel spectra. These computational kernels are implemented in C++ for maximum performance and exposed through Python interfaces, enabling seamless integration with modern machine learning workflows. HiGP also includes analytically derived gradient computations for efficient hyperparameter optimization, avoiding the inefficiencies of automatic differentiation in iterative solvers. By serving as a reusable numerical engine, HiGP complements existing GP frameworks such as GPJax, KeOps, and GaussianProcesses.jl, providing a reliable and scalable computational backbone for large-scale Gaussian Process regression and classification.

HiGP هو حزمة بايثون عالية الأداء تم تقديمها حديثًا تهدف إلى تحسين قابلية التوسع لعمليات Gaussian (GP)، والتي تُستخدم على نطاق واسع في مهام الانحدار والتصنيف نظرًا لمرونتها وقدرتها على قياس عدم اليقين. تتناول الحزمة التحديات الحسابية الكبيرة المرتبطة بعمليات مصفوفة النواة، والتي تقليديًا لها تعقيد مكعب.

HiGP es un nuevo paquete de Python de alto rendimiento diseñado para mejorar la escalabilidad de los Procesos Gaussianos (GP), que son ampliamente utilizados para tareas de regresión y clasificación debido a su flexibilidad y capacidad para cuantificar la incertidumbre. El paquete aborda los desafíos computacionales significativos asociados con las operaciones de matriz de núcleo, que tradicionalmente tienen una complejidad cúbica.

HiGP est un nouveau package Python haute performance conçu pour améliorer l'évolutivité des processus gaussiens (GP), largement utilisés pour des tâches de régression et de classification en raison de leur flexibilité et de leur capacité à quantifier l'incertitude. Le package s'attaque aux défis computationnels significatifs associés aux opérations de matrice noyau, qui ont traditionnellement une complexité cubique.

HiGP is a newly introduced high-performance Python package designed to enhance the scalability of Gaussian Processes (GPs), which are widely utilized for regression and classification tasks due to their flexibility and ability to quantify uncertainty. The package addresses the significant computational challenges associated with kernel matrix operations, which traditionally have a cubic complexity.

HiGP: A high-performance Python package for Gaussian Process

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A Highly Configurable Framework for Large-Scale Thermal Building Data Generation to drive Machine Learning Research

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