NHM
Gaussian estimates on networks with applications to optimal control
Luca Di Persio Giacomo Ziglio
Networks & Heterogeneous Media 2011, 6(2): 279-296 doi: 10.3934/nhm.2011.6.279
We study a class of reaction-diffusion type equations on a finite network with continuity assumptions and a kind of non-local, stationary Kirchhoff's conditions at the nodes. A multiplicative random Gaussian perturbation acting along the edges is also included. For such a problem we prove Gaussian estimates for the semigroup generated by the evolution operator, hence generalizing similar results previously obtained in [21]. In particular our main goal is to extend known results on Gaussian upper bounds for heat equations on networks with local boundary conditions to those with non-local ones. We conclude showing how our results can be used to apply techniques developed in [13] to solve a class of Stochastic Optimal Control Problems inspired by neurological dynamics.
keywords: Optimal stochastic control. Stochastic partial differential equations on networks Gaussian estimates
EECT
Optimal control for the stochastic FitzHugh-Nagumo model with recovery variable
Francesco Cordoni Luca Di Persio
Evolution Equations & Control Theory 2018, 7(4): 571-585 doi: 10.3934/eect.2018027

In the present paper we derive the existence and uniqueness of the solution for the optimal control problem governed by the stochastic FitzHugh-Nagumo equation with recovery variable. Since the drift coefficient is characterized by a cubic non-linearity, standard techniques cannot be applied, instead we exploit the Ekeland's variational principle.

keywords: Stochastic FitzHugh-Nagumo equation tochastic optimal control Ekeland's variational principle stochastic partial differential equations

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