Hybrid Physics-Informed Machine Learning for Multi-Vector Energy System Optimisation

Applications are invited for a self-funded, 3 year full-time or 6 year part-time PhD project.

The PhD will be based in the School of Electrical and Mechanical Engineering and will be supervised by Dr Shamsul Masum, Dr Shanker Prabhu, and Dr Anton Hettiarachchige Don.

The increasing complexity of modern energy systems, integrating electricity, heat, gas, and hydrogen vectors alongside intermittent renewables, demands advanced modelling and control approaches that can operate in real-time whilst maintaining physical plausibility. Whilst pure data-driven artificial intelligence methods offer computational speed and pattern recognition capabilities, they lack interpretability and can violate fundamental physical laws. Conversely, traditional physics-based models provide rigorous guarantees but struggle with computational tractability and parameter uncertainty in large-scale systems. This research addresses the critical need for hybrid approaches that combine the strengths of both paradigms.

Multi-vector energy systems present unique challenges: tight coupling between energy carriers, diverse temporal dynamics spanning milliseconds to seasons, nonlinear component behaviours, and operational constraints spanning technical, economic, and regulatory domains. Current optimisation frameworks either rely on computationally expensive detailed simulation or employ oversimplified data-driven surrogates that may produce physically infeasible solutions. Neither approach adequately addresses the real-time decision-making requirements of future decentralised energy infrastructure.

This research proposes to develop novel hybrid modelling architectures that integrate physical principles with machine learning capabilities. The challenge lies in balancing model accuracy, computational efficiency, constraint satisfaction, and interpretability, all essential requirements for deployment in safety-critical energy systems. The approach must handle uncertainties in renewable generation, demand patterns, and system parameters whilst guaranteeing physically admissible solutions.

Validation will employ operational data from energy networks experiencing high renewable penetration and complex multi-vector interactions. The research must demonstrate applicability across diverse operational scenarios, including normal operation, stress conditions, and edge cases not represented in training data. Particular emphasis will be placed on model interpretability and explainability to enable regulatory acceptance and operator confidence.

Research outcomes will advance theoretical understanding of physics-informed learning for energy applications and provide practical frameworks for managing increasingly complex energy infrastructure in the UK's transition to net-zero.

Fees and funding

Visit the research subject area page for fees and funding information for this project.

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.

How to apply

We’d encourage you to contact Dr Shamsul Masum (shamsul.masum@port.ac.uk) to discuss your interest before you apply, quoting the project code.

If you want to be considered for this self-funded PhD opportunity you must quote project code SEM10450526 when applying.