118 citations · 361 across the 15 of their papers we have counts for
16 papers
Latent-Space Non-Linear Model Predictive Control for Partially-Observable Systems
Luigi Marra, Onofrio Semeraro, Lionel Mathelin +2
This work presents a scalable control framework based on nonlinear Model Predictive Control for high-dimensional dynamical systems. The proposed approach addresses the key challeng…
Learning non-Markovian Dynamical Systems with Signature-based Encoders
Eliott Pradeleix, Rémy Hosseinkhan-Boucher, Alena Shilova +2
Neural ordinary differential equations offer an effective framework for modeling dynamical systems by learning a continuous-time vector field. However, they rely on the Markovian a…
Increasing Information for Model Predictive Control with Semi-Markov Decision Processes
Rémy Hosseinkhan-Boucher, Onofrio Semeraro, Lionel Mathelin
Recent works in Learning-Based Model Predictive Control of dynamical systems show impressive sample complexity performances using criteria from Information Theory to accelerate the…
Evidence on the Regularisation Properties of Maximum-Entropy Reinforcement Learning
Rémy Hosseinkhan-Boucher, Onofrio Semeraro, Lionel Mathelin
The generalisation and robustness properties of policies learnt through Maximum-Entropy Reinforcement Learning are investigated on chaotic dynamical systems with Gaussian noise on…
Mean flow data assimilation using physics-constrained Graph Neural Networks
M. Quattromini, M. A. Bucci, S. Cherubini +1
Despite their widespread use, purely data-driven methods often suffer from overfitting, lack of physical consistency, and high data dependency, particularly when physical constrain…
Neural delay differential equations: learning non-Markovian closures for partially known dynamical systems
Thibault Monsel, Onofrio Semeraro, Lionel Mathelin +1
Recent advances in learning dynamical systems from data have shown significant promise. However, many existing methods assume access to the full state of the system -- an assumptio…
Enhancing Data-Assimilation in CFD using Graph Neural Networks
Michele Quattromini, Michele Alessandro Bucci, Stefania Cherubini +1
We present a novel machine learning approach for data assimilation applied in fluid mechanics, based on adjoint-optimization augmented by Graph Neural Networks (GNNs) models. We co…
Time and State Dependent Neural Delay Differential Equations
Thibault Monsel, Onofrio Semeraro, Lionel Mathelin +1
Discontinuities and delayed terms are encountered in the governing equations of a large class of problems ranging from physics and engineering to medicine and economics. These syst…
Active learning of data-assimilation closures using Graph Neural Networks
Michele Quattromini, Michele Alessandro Bucci, Stefania Cherubini +1
The spread of machine learning techniques coupled with the availability of high-quality experimental and numerical data has significantly advanced numerous applications in fluid me…
Curriculum learning for data-driven modeling of dynamical systems
Alessandro Bucci, Onofrio Semeraro, Alexandre Allauzen +2
The reliable prediction of the temporal behavior of complex systems is key in numerous scientific fields. This strong interest is however hindered by modeling issues: often, the go…
Control of chaotic systems by Deep Reinforcement Learning
Michele Alessandro Bucci, Onofrio Semeraro, Alexandre Allauzen +3
Deep Reinforcement Learning (DRL) is applied to control a nonlinear, chaotic system governed by the one-dimensional Kuramoto-Sivashinsky (KS) equation. DRL uses reinforcement learn…
On the relevance of Reynolds stresses in resolvent analyses of turbulent wall-bounded flows
Pierluigi Morra, Onofrio Semeraro, Dan S. Henningson +1
The ability of linear stochastic response analysis to estimate coherent motions is investigated in turbulent channel flow at friction Reynolds number Re = 1007. The analysis is…
Resolvent-based modelling of coherent wavepackets in a turbulent jet
Lutz Lesshafft, Onofrio Semeraro, Vincent Jaunet +2
Coherent turbulent wave-packet structures in a jet at Reynolds number 460000 and Mach number 0.4 are extracted from experimental measurements and are modeled as linear fluctuations…
Full-order optimal compensators for flow control: the multi-input case
Onofrio Semeraro, Jan Oscar Pralits
Flow control has been the subject of numerous experimental and theoretical works. In this numerical study, we analyse full-order, optimal controllers for large dynamical systems in…
Qualitative dynamics of wavepackets in turbulent jets
Onofrio Semeraro, Francois Lusseyran, Luc Pastur +1
It has long been established that turbulent jets comprise large-scale coherent structures, now more commonly referred to as "wavepackets". These structures exhibit a remarkable spa…
Adaptive and model-based control theory applied to convectively unstable flows
N. Fabbiane, O. Semeraro, S. Bagheri +1
Research on active control for the delay of laminar-turbulent transition in boundary layers has made a significant progress in the last two decades, but the employed strategies hav…