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With the continuous growth of global energy demand, processes from power generation to electronics cooling become vitally important. The role of heat transfer in these processes is crucial, facilitating effective monitoring, control, and optimisation. Therefore, advancements and understanding of heat transfer directly correlate to system performance, lifespan, safety, and cost-effectiveness, and they serve as key components in addressing the world's increasing energy needs.

The field of heat transfer faces the challenge of needing intensive studies while retaining fast computational speeds to allow for system optimisation. While advancements in computational power are significant, current numerical models lack in handling complex physical problems such as ill-posed. The domain of heat transfer is rapidly evolving, driven by a wealth of data from experimental measurements and numerical simulations. This data influx presents an opportunity for machine learning techniques, which can be used to harness meaningful insights about the underlying physics.

Therefore, the current thesis aims to the explore machine learning methods concerning heat transfer problems. More precisely, the study looks into advanced algorithms such as deep, convolutional, and physics-informed neural networks to tackle two types of heat transfer: subcooled boiling and convective heat transfer. The thesis further addresses the effective use of data through transfer learning and optimal sensor placement when available data is sparse, to learn the system behaviour. This technique reduces the need for extensive datasets and allows models to be trained more efficiently. An additional aspect of this thesis revolves around developing robust machine learning models. Therefore, significant efforts have been directed towards accounting for the uncertainty present in the model, which can further illuminate the model’s behaviour. This thesis shows the machine learning model's ability for accurate prediction. It offers insights into various parameters and handles uncertainties and ill-posed problems. The study emphasises machine learning's role in optimising heat transfer processes. The findings highlight the potential of synergistic application between traditional methodologies and machine learning models. These synergies can significantly enhance the design of systems, leading to greater efficiency.