NWTF AI-Enabled Wind-Tunnel Experiments Forum

Date:

14th September 2026

Location:

London

Organisation:

Imperial College London

Host(s):

Dr Georgios Rigas, Department of Aeronautics, Imperial College London and Dr Sean Symon, University of Southampton

Aim
The forum explored the intersection of Artificial Intelligence and experimental fluid mechanics, highlighting emerging opportunities in autonomous wind-tunnel testing, digital twins, real-time sensing and actuation, reinforcement learning, and data-driven approaches to accelerate aerodynamic discovery.

Target Audience
PhD students, postdoctoral researchers, academics, facility managers and industry researchers working in experimental fluid mechanics, aerodynamics, flow control, wind-tunnel testing, machine learning, robotics, digital twins and related fields.

Agenda / Overview
The discussions throughout the day highlighted the growing potential of AI-enabled experimental campaigns and the importance of collaboration between academia and industry in shaping the future of aerodynamic research.

Downloadable presentations are highlighted in the event programme below:

Session	Presentation and Presenter
Welcome & Vision for AI-Enabled Experimental Facilities	Dr Georgios Rigas (Imperial College London)
AI-Enabled Experimental Measurements & Flow Reconstruction	PIGLET: Physics-Informed Gaussian Process Regression for the Learning and Estimation of Turbulence – John Lawson, University of Southampton Development and Application of Omnidirectional PIV Measurements for Wind Tunnel Testing – Alex Charogiannis, LaVision UK Efficient Mean Flow Reconstruction of Sparse Experimental Data – Uttam Cadambi Padmanaban, University of Southampton Physics-Informed Neural Networks for Super-Resolution of PIV Data and Flow Reconstruction – Roman Klopsch, Imperial College London Physics-Defined Generative Priors for Real-Time Wind-Tunnel Flow Field Estimation – Pranay Seshadri, Hendon AI
AI for Intelligent Experiments & Digital Twins	Machine Learning for Data Assimilation – Prof. J. Nathan Kutz, Autodesk Research, London, UK Development of a Wind Tunnel Digital Twin with Predictive Capability of Aerodynamic Data – Ruth Hendry, University of the West of England A Data-Driven Framework for Optimal Pressure Sensor Placement in Wind-Tunnel Testing – Caio Augusto Dias, Delft University of Technology, The Netherlands Optimal Experimental Design: Wind-Tunnel-in-the-Loop Bayesian Optimisation for Vehicle Aerodynamics – Isabella Fumarola, Imperial College London Catesby Tunnel — Exploiting High-Quality Experimental Data for AI at Catesby Tunnel – Jon Paton, Catesby Projects
Poster Flash Talks	Cluster-Based Dynamic Stall Detection Using Time-Delay Embedding – Eimear Curran, University of Bath Fast Flow Reconstruction from Sparse Data – Yuanye Zhou, University of Edinburgh From Kinematics to Flow Fields: A POD-LSTM Reduced-Order Model for Dynamic Stall – Christian Toma, University of Southampton Aerodynamic Load Reconstruction of an Aeroplane Wing Using Limited Pressure Sensors – Tafara Makuni, Imperial College London AI-Enabled Monitoring for Wind-Tunnel Experiments – Görkem Mahir Eriş, Middle East Technical University (METU), Türkiye ROM-based Pressure Prediction for a Pitching Airfoil – Lucas Schneeberger, Universidad Carlos III de Madrid, Spain
Panel Discussion: What should the next generation of AI-enabled experimental facilities look like?	Chair – Dr Georgios Rigas Bharath Ganapathisubramani, Professor at University of Southampton and Chair of the National Wind Tunnel Facility Anna Perry, Lead Wind Tunnel Methodology Engineer, Williams F1 Jon Paton, Managing Director, Catesby Projects Harry Simpson, Senior Technologist in Aerodynamics, Aerospace Technology Institute (ATI) Nick Simmonds, Principal Simulation Engineer, PhysicsX
AI for Engineering Discovery & Control	Giving Wind Farms a Power-Up with AI – Craig Thompson, Imperial College London Data-Driven Predictions of Unsteady Flows: From Visualising Forces Using Tufts to Optimal Foil Manoeuvres – Prof. Bharathram Ganapathisubramani, University of Southampton Autonomous Real-Time Control for Turbulent Systems – Junjie (Jacky) Zhang, Imperial College London Data-Driven Modelling of Flight Trajectories Through Wind Tunnel Measurements – Olaf Marxen, University of Surrey
Physical AI & Autonomous Experimentation	Active Operator Learning and Data Strategy for Large Physics Models – Nick Simmonds, PhysicsX Coachable Agents for Interactive Gameplay and Directions for Flow Control – Shruti Mishra, University of Cambridge Robotic Wind Tunnel Experiments: Learning to Fly – Yekaterina (Katya) Goodwin, Imperial College London
General Discussion, Future Opportunities & Closing Remarks	Open floor: research priorities, shared datasets and benchmarks, facility access, and routes to funding and collaboration

 

Event Programme

The programme also provided time for networking and collaboration, enabling connections with researchers, facility leads and industry partners across the NWTF network. 

NWTF AI-Enabled Wind Tunnel Experiments Forum Full Programme

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