Hybrid AI and physics modelling approaches for hygrothermal behavior of bio-based building materials: challenges, limitations and perspectives

The hygrothermal behavior of bio-based building materials plays a central role in determining indoor comfort, energy performance, and durability. Conventional physical models, grounded in conservation laws, provide interpretability and robustness but often struggle with hysteresis effects, heterogeneity, and computational cost. Conversely, data-driven machine learning (ML) approaches offer flexibility and efficiency but lack physical consistency and interpretability. In response to these limitations, hybrid physics-AI models have recently emerged as transformative tools. This review critically examines three ML paradigms: Physics-Informed Neural Networks (PINNs), Physics-Informed Graph Neural Networks (PIGNNs), and Universal Differential Equations (UDEs), and evaluates their potential for simulating coupled heat and moisture transfer in porous bio-based envelopes. PINNs demonstrate high accuracy under sparse data conditions but remain limited by training cost and scalability. PIGNNs offer scalability and adaptability to irregular geometries, enabling large-scale or real-time simulations of building envelopes, but face challenges in representing hysteresis effect. UDEs provide balanced trade-off by embedding physics while correcting unmodeled nonlinearities such as sorption hysteresis and multiscale porosity. By offering a critical state-of-the-art analysis of recent advances, this review identifies current limitations, experimental requirements, and future directions for the deployment of hybrid AI–physics approaches in hygrothermal analysis. It concludes by positioning these methods as a roadmap for next-generation digital twins of bio-based materials, supporting predictive design, performance monitoring, and sustainable building practices.