The recent advancements in the research area have seen a significant shift towards integrating multiple modeling paradigms to enhance predictive accuracy and interpretability. A notable trend is the fusion of molecular and process-level information in environmental impact assessments, particularly in predicting Global Warming Potential (GWP). This approach not only improves accuracy but also provides transparent insights into the factors influencing GWP, facilitating more informed decision-making in sustainability assessments. Additionally, the field is witnessing a growing interest in combining symbolic neural networks with traditional physics-based models, especially in domains like building physics and energy systems. This hybridization aims to leverage the strengths of both data-driven and physics-based models, leading to more robust and explainable predictions. Furthermore, there is a focus on developing resilient structural designs using advanced materials like Ultra-High Performance Concrete (UHPC), where the emphasis is on promoting failure modes that offer warning signs through high ductility and visible cracks. Lastly, the integration of phenomenological models with deep learning techniques in constitutive modeling is gaining traction, offering a balance between interpretability and generalization capabilities, even with limited and noisy data.

Noteworthy papers include one that introduces a Kolmogorov-Arnold Network (KAN) for GWP prediction, significantly improving accuracy and interpretability by integrating molecular and process-level data. Another paper stands out for its exploration of hybrid modeling approaches in building energy systems, demonstrating superior performance through a real-world case study. Lastly, a study on the design of UHPC beams highlights innovative methods to promote ductile failure modes, enhancing structural resilience.