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July 3, 2026Journal of Building Performance Simulation

From surrogate modelling to AI: how machine learning is transforming building simulation

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Authors

RERalph Evins

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Overview

Explores how machine learning enhances building simulations, suggesting implications for sustainable designs.

Key Points

  • The aim is to investigate the impact of machine learning and surrogate modelling on building simulations.
  • Reviewed recent developments in surrogate modelling techniques.
  • Discussed the applications of machine learning in building performance simulation.
  • Explored the challenges and benefits of generative AI tools in this sector.
  • Surrogate modelling can replicate physics-based simulations rapidly, facilitating large-scale analyses.
  • High accuracy in predictions is achievable with machine learning, provided the model is appropriately designed.
  • The integration of AI tools presents both substantial benefits and critical challenges for sustainable design practices.

Cite This Study

Ralph Evins (2026) studied this question.

synapsesocial.com/papers/6a47513c5c29257aa257887bhttps://doi.org/10.1080/19401493.2026.2694582
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