## 3 Results  
The results obtained from the analysis of the papers retrieved from the string K4 reveal that the annual production of scientific papers regarding metamodelling and surrogate modelling techniques for the energy performance and operational carbon assessment in buildings has an average annual growth rate of 8%. Two notable observations from Figure 4 are the exponential growth around 2010 and the peak in publications on the topic in 2019. Several factors could have contributed to this surge. The early 2010s saw a rise in environmental advocacy groups and movements, bringing attention to the importance of sustainable building practices to combat climate change. Moreover, after the 2010s--called, by [PERSON] [21], the \"Deep Learning Revolution\" period--several other events could have influenced this growth. For instance, the Deep Learning Breakthroughs. The early 2010s marked significant advancements in deep learning. The introduction of architectures like AlexNet in 2012 revolutionised the field of computer vision. The triumph of this convolutional neural network in the ImageNet Large Scale Visual Recognition Challenge served as a catalyst, accelerating the embrace of deep learning methodologies across diverse fields, building performance simulations included. Furthermore, in 2014, [PERSON], alongside his team, unveiled Generative Adversarial Networks (GANs). GANs opened up new possibilities for generating synthetic data. In the context of building simulations, this could mean generating synthetic building designs or performance metrics to augment datasets. On the other hand, 2019 marked another significant step in the field thanks to several factors such as:  
1. Technological Advancements: By 2019, there were significant breakthroughs in computational capabilities, particularly in the realm of AI and machine learning. The development and dissemination of tools like TensorFlow and PyTorch, which made deep learning more accessible, played a pivotal role. Additionally, the proliferation of cloud computing platforms, such as AWS, provided researchers with affordable,  
Figure 3: Correlation analysis—interconnected keyword network for AI-driven design. scalable computational resources, enabling more intricate simulations and models related to building energy performance. 2. Regulatory Changes: Around 2019, the European Union introduced the 'Clean Energy for All Europeans' package--which includes the EPBD. 3. Funding and Grants: In 2019, international bodies like the United Nations and the European Union emphasised the sustainable development goals, leading to increased funding opportunities for research on sustainable infrastructure and green building practices. Such financial injections often catalyse academic research, leading to a surge in publications. After noting the growing interest in the researched topics, we proceeded with a more in-depth semantic analysis of the word occurrences across the titles, keywords, and abstracts of the papers obtained from the string K4, returning an initial identification of the most widely addressed topics. The treemap in Figure 5 shows the frequency of terms in the surveyed literature, helping in identifying areas of concentrated research and emerging trends. For clarity and structured interpretation, we categorised these keywords into six thematic clusters, as depicted in the list below and visually represented in Figure 6.  
* Performance--incorporating terms such as simulation, global optimisation, and parameters. * Optimisation--covering concepts like framework, algorithm, regression, and reliability. * Carbon--highlighting terms like impact, construction, life-cycle assessment, and emissions. * Consumption--encompassing terms like consumption, prediction, neural network, and surrogate model. * System--including descriptors like system, water, and storage. * Sensitivity analysis--capturing concepts like calibration, tool, metamodelling techniques, and decision-making.