Aims and objectives

Aim: design, validate, and deploy robust surrogate models capable of delivering rapid and reliable predictions in uncertain, high-stakes engineering environments.

Objectives:

1. Develop robust surrogate model architectures. Implement and compare surrogate modelling approaches (Imprecise Gaussian Processes, Interval Predictor Models, Physics-Informed Neural Networks). Design architectures that prioritise transparency, error bounds and interpretability.

2. Integrate uncertainty quantification from sparse or imperfect data. Investigate how epistemic and aleatory uncertainties can be modelled with minimal data. Use probabilistic bounds and interval analysis to preserve conservatism without excessive over-approximation.

3. Accelerate risk-critical simulations. Replace expensive high-fidelity simulations (thermal-fluid, structural fatigue, multiphase flow) with validated surrogates. Benchmark performance on fidelity, runtime and safety margin preservation.

4. Integrate robust surrogates into digital twins. Embed surrogates in digital twin pipelines and scenario stress-testing environments.

5. Industrial case study. Co-develop use cases (fatigue risk estimation, fluid flow control, onboard diagnostics) with the industrial partner. Participate in secondments to gain feedback from engineers and simulation experts.

6. Contribute to open-source infrastructure and academic dissemination. Develop reusable, documented codebases with well-defined uncertainty propagation and validation modules. Target top-tier journals and present findings at academic and practitioner conferences.

Alignment to STAND-UP impact targets

>50% reduction in overall build or decommissioning process time (not applicable)

>40% reduction in maintenance time (not applicable)

>30% reduction in person hours on builds (not applicable)