5 papers
Two-stage Residual Learning for Modelling Strength-Informed Optimisation of Thermal Systems
Talha Ansar, Waqar Muhammad Ashraf, Abdulelah S. Alshehri +1
Aggregate performance metrics do not identify local modelling inaccuracies in machine learning (ML) surrogates used for thermal-system optimisation. This study assesses local model…
Data-driven Optimal Sensor Placement for Aerosol Monitoring in the UK
Talha Ansar, Waqar Muhammad Ashraf, Alejandro Coca-Castro +3
We develop a data-driven sensor-placement framework and apply it to aerosol monitoring in the United Kingdom (UK). A trained convolutional Gaussian neural process is embedded withi…
Data-driven Bi-level Optimization of Thermal Power Systems with embedded Artificial Neural Networks
Talha Ansar, Muhammad Mujtaba Abbas, Ramit Debnath +2
Industrial thermal power systems have coupled performance variables with hierarchical order of importance, making their simultaneous optimization computationally challenging or inf…
From drift to adaptation to the failed ml model: Transfer Learning in Industrial MLOps
Waqar Muhammad Ashraf, Talha Ansar, Fahad Ahmed +3
Model adaptation to production environment is critical for reliable Machine Learning Operations (MLOps), less attention is paid to developing systematic framework for updating the…
Neural Network-enabled Domain-consistent Robust Optimisation for Global CO Reduction Potential of Gas Power Plants
Waqar Muhammad Ashraf, Talha Ansar, Abdulelah S. Alshehri +3
We introduce a neural network-driven robust optimisation framework that integrates data-driven domain as a constraint into the nonlinear programming technique, addressing the overl…