Summary
I am a Senior Lecturer in the Department of Aeronautics at Imperial College London. Before joining IC in 2019, I was a Postdoctoral Scholar at Caltech and the University of Cambridge. I received a Ph.D. in Aeronautical Engineering from Imperial College in 2015.
My research focuses on engineering science and technology at the intersection of fluid mechanics, control, energy, and environment. I am interested in the fundamental understanding and control-oriented modelling of transitional and turbulent flows encountered in the aerospace and automotive industry. I am particularly interested in the development and implementation of active and passive control schemes, which reduce noise, aerodynamic drag, structural fatigue. Control schemes are evaluated through high-fidelity numerical simulations and state-of-the-art experiments at the Imperial Aero Wind Tunnel facilities.
Publications
Research Opportunities
- PhD. Available opportunities are advertised on the departmental website (link).
- PhD. Students with a strong track record in engineering/math disciplines are encouraged to apply for the IC President's PhD Scholarship. Three rounds, deadline: ~ 8 Nov - 20 March.
Publications
Journals
Scherding C, Rigas G, Sipp D, et al., 2023, Data-driven framework for input/output lookup tables reduction: Application to hypersonic flows in chemical nonequilibrium, Physical Review Fluids, Vol:8, ISSN:2469-990X
Poulain A, Content C, Sipp D, et al., 2023, BROADCAST: A high-order compressible CFD toolbox for stability and sensitivity using Algorithmic Differentiation, Computer Physics Communications, Vol:283, ISSN:0010-4655, Pages:1-23
Sliwinski L, Rigas G, 2023, Mean flow reconstruction of unsteady flows using physics-informed neural networks, Data-centric Engineering, Vol:4, ISSN:2632-6736, Pages:1-23
Towne A, Rigas G, Kamal O, et al., 2022, Efficient global resolvent analysis via the one-way Navier–Stokes equations, Journal of Fluid Mechanics, Vol:948, ISSN:0022-1120, Pages:1-45
Conference
Kelshaw D, Rigas G, Magri L, Physics-informed CNNs for super-resolution of sparse observations on dynamical systems, 36th conference on Neural Information Processing Systems (NeurIPS)