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August 8, 2026Journal of Building Performance SimulationOpen Access

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RERalph Evins

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Overview

Randomized trial explores surrogate modelling to improve simulation efficiency, suggesting significant cost reductions.

Key Points

  • This research aims to establish surrogate modelling techniques to replicate complex physics-based simulations efficiently.
  • Utilized machine learning algorithms to train models for simulating EnergyPlus outcomes.
  • Compared computational costs of surrogate models against traditional physics-based simulations.
  • Conducted analyses to evaluate performance and efficiency of the proposed modelling approach.
  • Surrogate models achieved up to 90% reduction in computational time compared to traditional simulations.
  • Model accuracy maintained with minimal loss in fidelity, highlighted by R-squared values greater than 0.8 for key performance metrics.
  • Demonstrated significant cost-saving potential in energy simulations, indicating broader applicability.

Cite This Study

Ralph Evins (2026) studied this question.

synapsesocial.com/papers/6a76da3bf12abadc79814907https://doi.org/10.1080/19401493.2026.2705019
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