Program
We expect time slots for the presenters of 20 minutes: 15 minutes presentation + 4-5 minutes discussion. For keynote lectures the 50-minutes slot is split in 40 minutes presentation plus 10 minutes of discussion.
The full program can be downloaded here
Day 1 - Data-driven modelling and reduced-order models
08.30 Registration
Session 1 - Data-driven modelling 1
Chairman: Discetti, S.
09.10 - Haller, G. - Data-driven modeling and control via spectral submanifolds
10.00 - King, J. - Parametric spectral submanifolds across Hopf bifurcations with applications to fluid dynamics
10.20 - Pastur, L. - Bistable flow dynamics of airfoil stall under varying angle of attack: A stochastic model with multiplicative noise
Session 2 - Data-driven modelling 2
Chairman: Discetti, S.
11.10 - Franchini, A. - Mean resolvent analysis of a stochastic axisymmetric jet
11.30 - Fossella, F. - Multiscale data assimilation in turbulence
11.50 - Lahgazi, A. - Uncertainty-aware latent-dynamics ROM for state estimation in unsteady flows
12.10 - Di Domenico, V. - POD-Galerkin reduced order method for unsteady, turbulent, buoyant flows with parametric boundary conditions
Session 3 - Data-driven modelling 3
Chairman: Mendez, M. A.
14.00 - Fink, O. - Momentum-conserving physics-informed graph neural networks for dynamical systems
14.50 - Lebrec, E. - Sparse data conditioned diffusion model for PDE solving
15.10 - Tirelli, I. - From spikes to dynamics: SNN-based latent space identification for wake flow prediction
Session 4 - Data-driven modelling 4
Chairman: Mendez, M. A.
16.00 - Tonioni, N. - Navigating the intermittency: A generative surrogate for long-horizon forecasting of minimal flow unit from sparse measurements
16.20 - Sanmiguel-Vila, C. - Sparse-sensing-state estimation of vortex-gust airfoil interactions
16.40 - Klotz, L. - Dominant recurrent carrier of turbulence within spatially-localized turbulent structures
17.00 - Castelletti, M. - Estimating turbulent channel flow from wall measurements
Day 2 - Model-based and model free control
Session 5 - Model-based control 1
Chairman: Rigas, G.
09.00 - Sipp, D. - Koopman-based model-order reduction: coherent structures, frequencies and damping rates in stochastic cavity flow
09.50 - Marra, L. - Model predictive control in latent coordinates for partially observable systems
10.10 - Leclercq, C. - Increasing non-linear stability of flows using optimal control
Session 6 - Model-based control 2
Chairman: Leclercq, C.
11.00 - Gilotte, P. - Drag and lift optimization with a surrogate model
11.20 - Marra, L. - Safe learning by combination of MPC and reinforcement learning
11.40 - Ozan, D.E. - A sliding mode observer perspective on chaos synchronisation of turbulent flows
12.00 - Cornejo-Maceda, G.Y. - Actuation manifold for control of a flapping wing under dynamic flow conditions
12.20 - Rodriguez-Asensio, A. - Can we predict the transient trajectories of the actuated fluidic pinball?
Session 7 - Reinforcement Learning 1
Chairman: Sayadi, T.
14.00 - Chaturvedi, V. - Reinforcement learning for olfactory navigation in a turbulent flow
14.20 - Qiu, J. - Adaptive shape control for microswimmer navigation in turbulence
14.40 - Zhou, Z. - Two-way regulation of turbulent heat transfer via interpretable bang-bang control discovered by reinforcement learning
15.00 - Mohammadikalakoo, B. - Data-driven suppression of naturally developing Tollmien–Schlichting waves using plasma actuation
15.20 - Tomasetto, M. - Physics-enhanced reinforcement learning for real-time optimal control of dynamical systems
Social activities
Day 3 - Control theory and model free control
Session 8 - Control theory
Chairman: Semeraro, O.
09.20 - Marcos, A. - Compressed sensing for launchers and satellites’ parameter and fault estimation
09.40 - Kern, J.S. - Control-then-reduce approach to optimal control using dynamical low-rank approximation
10.00 - Schena, L. - Reinforcement twinning for wind farm flow control using differentiable dynamic wake models and learned value corrections
Session 9 - Reinforcement Learning 2
Chairman: Mathelin, L.
10.50 - Manganelli, F. - Multi-agent reinforcement learning for wind farm optimization in atmospheric boundary layer
11.10 - Zhang, J. - Learning observers for partially observable flow control
11.30 - Ozan, D.E. - Data-assimilated model-informed reinforcement learning
11.50 - Lecomte, Y. - Bayesian reinforcement twinning: a multi-fidelity framework for reciprocal learning between digital twins and control
12.10 - Suarez, G. - Active flow control via model-based reinforcement learning
12.30 - Paolillo, G. - Deep reinforcement learning for combined shape and tangential blowing optimization with wind tunnel validation
Session 10 - Concluding session
Chairman: Mortazavi, I.
14.00 - Hachem, E. - Coupling reinforcement learning and CFD to support decision making