# Physics-informed machine learning for building performance simulation-A review of a nascent field

arXiv: https://arxiv.org/abs/2504.00937 · HTML: https://mlsanity.com/paper/2504.00937/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2025.

## Built on by (most cited first, 4 of 4)

- [Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling](https://mlsanity.com/paper/2507.17526/) (2025)
- [Physics-informed Modularized Neural Network for Advanced Building Control by Deep Reinforcement Learning](https://mlsanity.com/paper/2504.05397/) (2025)
- [Large Language Models for HVAC Operations in Building Energy Systems: A Critical Review of Methods, Applications, and Deployment Readiness](https://mlsanity.com/paper/2609.05314/) (2026)
- [The Smart Buildings Challenge: Learning Foundation Models for Real-World Optimization at Scale](https://mlsanity.com/paper/2610.18511/) (2026)

## Abstract (arXiv)

Building performance simulation (BPS) is critical for understanding building dynamics and behavior, analyzing performance of the built environment, optimizing energy efficiency, improving demand flexibility, and enhancing building resilience. However, conducting BPS is not trivial. Traditional BPS relies on an accurate building energy model, mostly physics-based, which depends heavily on detailed building information, expert knowledge, and case-by-case model calibrations, thereby significantly limiting their scalability. With the development of sensing technology and increased data availability, there is a growing attention and interest in data-driven BPS. However, purely data-driven models often suffer from limited generalization ability and a lack of physical consistency, resulting in poor performance in real-world applications. To address these limitations, recent studies have started to incorporate physics priors into data-driven models, a methodology called physics-informed machine learning (PIML). PIML is an emerging field with the definitions, methodologies, evaluation criteria, application scenarios, and future directions that remain open. To bridge those gaps, this study systematically reviews the state-of-art PIML for BPS, offering a comprehensive definition of PIML, and comparing it to traditional BPS approaches regarding data requirements, modeling effort, performance and computation cost. We also summarize the commonly used methodologies, validation approaches, application domains, available data sources, open-source packages and testbeds. In addition, this study provides a general guideline for selecting appropriate PIML models based on BPS applications. Finally, this study identifies key challenges and outlines future research directions, providing a solid foundation and valuable insights to advance R&D of PIML in BPS.