output
20202022
most citedIncentives, lockdown, and testing: from Thucydides's analysis to the COVID-19 pandemic

22 citations

5 papers

math.OC2022

Mean-field neural networks-based algorithms for McKean-Vlasov control problems *

Huyên Pham, Xavier Warin

This paper is devoted to the numerical resolution of McKean-Vlasov control problems via the class of mean-field neural networks introduced in our companion paper [25] in order to l…

math.OC2021

A level-set approach to the control of state-constrained McKean-Vlasov equations: application to renewable energy storage and portfolio selection

Maximilien Germain, Huyên Pham, Xavier Warin

We consider the control of McKean-Vlasov dynamics (or mean-field control) with probabilistic state constraints. We rely on a level-set approach which provides a representation of t…

math.OC2021

DeepSets and their derivative networks for solving symmetric PDEs

Maximilien Germain, Mathieu Laurière, Huyên Pham +1

Machine learning methods for solving nonlinear partial differential equations (PDEs) are hot topical issues, and different algorithms proposed in the literature show efficient nume…

q-bio.PE2020★ 22 cited

Incentives, lockdown, and testing: from Thucydides's analysis to the COVID-19 pandemic

Emma Hubert, Thibaut Mastrolia, Dylan Possamaï +1

In this work, we provide a general mathematical formalism to study the optimal control of an epidemic, such as the COVID-19 pandemic, via incentives to lockdown and testing. In par…

math.OC2020★ 2 cited

Decomposition of convex high dimensional aggregative stochasticcontrol problems

Adrien Séguret, Clémence Alasseur, J. Frédéric Bonnans +3

We consider the framework of convex high dimensional stochastic control problems, in which the controls are aggregated in the cost function. As first contribution, we introduce a m…