Two-stage Residual Learning for Modelling Strength-Informed Optimisation of Thermal Systems
Aggregate performance metrics do not identify local modelling inaccuracies in machine learning (ML) surrogates used for thermal-system optimisation. This study assesses local modelling strength through validation-based reliability screening and post-optimisation residual assessment. A two-stage residual learning (TSRL) framework, built on artificial neural networks (ANNs), is developed for modelling-strength-informed and domain-constrained optimisation of thermal systems. Within the TSRL framework, a base ANN predicts the process output, and a residual ANN learns the signed residuals left by the base ANN. The TSRL framework is implemented on two case studies of thermal systems, including ...
| Authors | Talha Ansar, Waqar Muhammad Ashraf, Abdulelah S. Alshehri et al. |
|---|---|
| Primary category | cs.LG |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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