Data-driven and physics-based modeling approaches and their integration in building digital twins: A systematic review
Bibliographic record
Abstract
Interest in digital twin technology has grown significantly within the building sector as part of the broader digital transformation in the architecture, engineering, and construction industry. A building digital twin is a virtual replica that captures a building’s static and dynamic behavior through data, information, and models. Digital twin models can be developed using data-driven or physics-based approaches, each with distinct advantages and limitations. Data-driven models can learn complex behaviors from data and scale well, but they require large datasets and often lack interpretability. In contrast, physics-based models offer interpretability and generalizability through fundamental principles but can be computationally demanding. Consequently, building digital twins can benefit greatly from integrating both approaches through hybrid modeling. However, the literature lacks a comprehensive analysis of integration strategies within building digital twins. This study addresses that gap by reviewing advances in data-driven and physics-based modeling and analyzing various integration levels. The results show that most studies rely on siloed models, using either approach independently without leveraging their complementary strengths. Some adopted sequential integration, where one model informs the other but lacks real-time or iterative feedback. A few achieved coupled integration, involving active data exchange and collaboration between models. Only three studies explored fusion integration, where both approaches are fully unified into a single model. Based on this review, a method is proposed for selecting the appropriate level of integration, considering factors such as data availability, interpretability, generalizability, and domain knowledge. Finally, key research gaps and future directions are identified to guide further work. • Reviews data-driven and physics-based modeling approaches in building DTs. • Examines varying levels of integration between data-driven and physics-based models. • Discusses the key trade-offs for each modeling approach and integration level. • Presents guidelines for selecting the appropriate integration level. • Identifies key research gaps to direct future research efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".