Research
My research develops AI methods for modelling, reasoning about, and designing complex physical systems. It connects high-fidelity simulation and scientific data with foundation models and agentic engineering workflows that remain verifiable, traceable, and accountable to human engineering judgement.
Current research
Research programme
Agentic computational engineering
AI systems that formulate engineering tasks; select and orchestrate numerical, analysis, and optimisation tools; verify results for physical consistency; preserve provenance and traceability; handle uncertainty and recover from failures; and retain human oversight for consequential decisions.
NASA Ames talkOpen scientific data
CAE ML Datasets
I lead and collaborate on large-scale, high-fidelity CFD datasets for reproducible machine-learning research. AhmedML, WindsorML, DrivAerML, and HiLiftAeroML cover automotive and aerospace configurations of increasing geometric and physical complexity.
Project websitePerspective paper
Fluid Intelligence
An analysis of scaling laws, data-generation cost, and the technical requirements for foundation models in computational fluid dynamics.
PaperCommunity workshops
I founded AutoCFD and serve on the HLPW organising committee, where I lead the AI/ML Technical Focus Group. These programmes establish shared test cases, evaluation methods, and community research priorities for computational aerodynamics.
Automotive CFD Prediction Workshop (AutoCFD)
Founder and organiser
An international workshop series coordinating community assessment and validation of CFD methods for road-vehicle aerodynamics.
AIAA CFD High-Lift Prediction Workshop (HLPW)
Organising committee and AI/ML Technical Focus Group lead
A collaborative workshop series advancing computational methods and validation practices for complex high-lift aircraft configurations.
Open engineering datasets
AhmedML
Scale-resolving CFD for 500 geometric variants of the Ahmed body.
WindsorML
GPU-native wall-modelled large-eddy simulations for 355 Windsor-body variants.
DrivAerML
Surface and volume flow data for 500 realistic road-car variants.
HiLiftAeroML
High-fidelity simulations across geometry variants and angles of attack for high-lift aircraft aerodynamics.
Research questions
Physical-system representations
How can models learn from high-fidelity simulations and generalise across geometries, operating conditions, and engineering tasks?
Tool-using engineering agents
How can agents select and orchestrate simulation, analysis, and optimisation tools while verifying physical consistency, preserving provenance, recovering from failures, representing uncertainty, and retaining appropriate human control?
Trustworthy evaluation
Which datasets and benchmarks demonstrate physical fidelity, engineering utility, computational efficiency, and out-of-distribution performance?
Research correspondence
I welcome correspondence from researchers working on related problems in industry and academia.