# Physics-Informed Surrogate Modelling in Fire Safety Engineering: A Systematic Review
## 2 Materials and Methods  
This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines [51]. An a priori protocol was developed prior to the review to ensure methodological transparency and consistency throughout the process. The aim of the review was to identify and synthesize current applications of machine learning-based modelling within the domain of FSE, with a particular focus on PISM. The review also sought to categorise the various strategies employed to integrate physical knowledge into machine learning models, and to identify key challenges and best practices observed in the literature. Studies were eligible for inclusion if they were (i) peer-reviewed primary research articles published in English that (ii) directly applied PISM or physics-informed machine learning (PIML) (iii) within any subdomain of FSE, and (iv) for which full-text articles could be obtained using the Ghent University academic licenses. No publication date restrictions were applied in order to capture the full breadth of the available literature. In the initial screening phase, review articles and purely data-driven modelling approaches without explicit physics integration were also retained in order to obtain a comprehensive view of the state-of-the-art in machine learning applications in FSE. Figure 5: PRISMA 2020 flow diagram summarising the identification, screening, eligibility assessment, and inclusion of studies in the systematic review on PISM applications in SFE. A comprehensive literature search was carried out from 1 December 2024 to 15 May 2025, across Scopus, ScienceDirect, ResearchGate, Google Scholar, and aRxiv. The search strategy was done considering keyword combinations related to physics-informed modelling, surrogate models, machine learning, and fire safety engineering. The specific search variations of \"physics-informed,\" \"physics-based,\" \"PIML,\" \"explainable machine learning\", \"Physics-enhanced\", \"Scientific machine learning\", and \"PISM,\" in combination with \"surrogate model,\" \"reduced-order model,\" \"machine learning,\" and \"fire safety.\" Approximately 220 records were retrieved in total. All references were imported into Mendeley for management, and duplicate entries were removed prior to screening. In addition to the database search, a snowballing approach was employed to ensure more comprehensive coverage of the literature. This involved both backward snowballing--screening the reference lists of included articles for additional relevant studies--and forward snowballing by reviewing newer publications that cited the included papers using Google Scholar and ResearchGate. This iterative process helped identify studies that may not have been captured through keyword searches alone, particularly those using less standardised terminology or published in interdisciplinary venues. The screening process was conducted in two stages. First, one reviewer (the first author) screened the titles and abstracts of all retrieved articles against the predefined eligibility criteria. Full-text articles were then reviewed to determine final inclusion (i.e., confirming that the article meets eligibility criteria (ii) and (iii)). In this stage, some of the papers were excluded [52; 53]. Studies were organised according to five subcategories of fire safety engineering, as defined in Section 2.1. Any disagreements regarding the inclusion of specific studies were resolved through discussion and consensus with the co-authors. The study selection process is summarised in the PRISMA 2020 flow diagram (Figure 5), which outlines the number of records retrieved, screened, excluded, and, ultimately, included in the final review. For each included study, detailed information was systematically extracted using a pre-designed data collection form.