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Research interests

Control of Partial Differential Equations
Control of Level Set Methods
Dynamic Mode Decomposition for Transitional flows
Wound Healing and tissue repair models
Level set methods in biological applications
Algorithms and scheduling for drones
Platform for managing drones
Employment of UAV for fighting wildfires 

Control of Level Set Methods

Level Set Methods (S. Osher, J. Sethian, J. of Computational Physics 1987) are a class of very popular methods to propagate interfaces. Our aim is to develop methodologies to control in time  fronts described by Level Set Methods. A project "C-LEVEL” was funded by US AFOSR, Air Force Office of Scientific Research. 

Development of algorithms and a scheduling dynamic system for managing drone swarms

M.A.R.S. is a patented platform developed by Inspire (Unige spin-off) that allows:

-  integrated logistic for drones deployment
– extended flight missions enabled by automatic battery      replacement system
– fractionable payload management system
– collaborative drones flight in swarm configuration

System for the employment of UAV for fighting wildfires 

The system allows to send a swarm of drones into the area of the fire and generates the equivalent of a continuous rain: 100 drones carrying 5 liters of extinguishing liquid and making a loop trail in 3 minutes, spreads in 6 minutes one cubic meter in an area of 100 square meters, corresponding to 1 cm of rain each 6 minutes. Our patented system guarantees a 24-hour service continuity achieved by automatic battery replacement/recharge system and fractionable payload.


Wound healing in Drosophila pupae

We study wound healing in collaboration with L. Neves de Almeida (Sorbonne Université) and with A. Jacinto, a biologist of the Lisboa University. We proposed continuous mathematical models where the wound is described by Level set methods. Currently, we are modeling a complex mechanism starting in the first minutes of wound healing.


Dynamic Mode Decomposition for fluid flows

The spatial and time behavior of fluid flows at different Reynolds numbers and stream turbulence intensity levels is analysed by combining dynamic mode decomposition and
moving horizon estimation in order to detect regime transitions. The norm of the residuals  in processing successive snapshots of the flow
velocity field shows a trend that is used to identify the change from stable and unstable

Selected Publications

A. Alessandri, P. Bagnerini, M. Gaggero, D. Lengani, D. Simoni, Dynamic Mode Decomposition for the Inspection of Three-Regime Separated Transitional Boundary Layers Using Least Squares Methods, Physics of Fluids, vol.31, n. 4, 2019.

P. Bagnerini, G. Fabrini, B. D. Hughes, T. Lorenzi, L. Neves de Almeida, Evolution of cancer cell populations under cytotoxic therapy and treatment optimisation: insight from a phenotype-structured model, Modélisation Mathématique Et Analyse Numérique (M2AN), to appear 2019.

A. Alessandri, P. Bagnerini, M. Gaggero, Optimal Control of Propagating Fronts by Using Level-Set Methods and Neural Approximations, IEEE Transactions on Neural Networks and Learning Systems, 30-3, pp 902-912, 2019.

A. Alessandri, P. Bagnerini, M. Gaggero, D. Lengani, D. Simoni, Moving horizon trend identification based on switching models for data driven decomposition of fluid flows, in 57th IEEE Conf. on Decision and Control, Miami, Florida, USA, pp. 2138–2143, 2018.

M. Dureau, A. Alessandri, P. Bagnerini, S. Vincent, Modeling and Identification of Amnioserosa Cell Mechanical Behavior by Using Mass-Spring Lattices, IEEE/ACM Transactions On Computational Biology And Bioinformatics, Vol. 14, n. 6, pp 1476-1481, 2017.



Dime, University of Genoa, via all'Opera Pia 15, 16145 Genova (Italy)
Office: ground floor


+39 0103536001


bagnerini at