Optimal control of a class of reaction-diffusion systems

The optimal control of a system of nonlinear reaction-diffusion equations is considered that covers several important equations of mathematical physics. In particular equations are covered that develop traveling wave fronts, spiral waves, scroll rings, or propagating spot solutions. Well-posedness of the system and differentiability of the control-to-state mapping are proved. Associated optimal control problems with pointwise constraints on the control and the state are discussed. The existence of optimal controls is proved under weaker assumptions than usually expected. Moreover, necessary first-order optimality conditions are derived. Several challenging numerical examples are presented that include in particular an application of pointwise state constraints where the latter prevent a moving localized spot from hitting the domain boundary.Eduardo Casas was partially supported by the Spanish Ministerio de Economía, Industria y Competitividad under Projects MTM2014-57531-P and MTM2017-83185-P. Christopher Ryll and Fredi Tröltzsch are supported by DFG in the framework of the Collaborative Research Center SFB 910, Project B6

Optimal control of stochastic FitzHugh-Nagumo equation

This paper is concerned with existence and uniqueness of solution for the the optimal control problem governed by the stochastic FitzHugh-Nagumo equation driven by a Gaussian noise. First order conditions of optimality are also obtained

Neural Network Approximation of Optimal Controls for Stochastic Reaction-Diffusion Equations

We present a numerical algorithm that allows the approximation of optimal controls for stochastic reaction-diffusion equations with additive noise by first reducing the problem to controls of feedback form and then approximating the feedback function using finitely based approximations. Using structural assumptions on the finitely based approximations, rates for the approximation error of the cost can be obtained. Our algorithm significantly reduces the computational complexity of finding controls with asymptotically optimal cost. Numerical experiments using artificial neural networks as well as radial basis function networks illustrate the performance of our algorithm. Our approach can also be applied to stochastic control problems for high dimensional stochastic differential equations and more general stochastic partial differential equations.Comment: 12 figure

Second-order sufficient conditions in the sparse optimal control of a phase field tumor growth model with logarithmic potential

his paper treats a distributed optimal control problem for a tumor growth model of viscous Cahn--Hilliard type. The evolution of the tumor fraction is governed by a thermodynamic force induced by a double-well potential of logarithmic type. The cost functional contains a nondifferentiable term in order to enhance the occurrence of sparsity effects in the optimal controls, i.e., of subdomains of the space-time cylinder where the controls vanish. In the context of cancer therapies, sparsity is very important in order that the patient is not exposed to unnecessary intensive medical treatment. In this work, we focus on the derivation of second-order sufficient optimality conditions for the optimal control problem. While in previous works on the system under investigation such conditions have been established for the case without sparsity, the case with sparsity has not been treated before. The results obtained in this paper also improve the known results on this phase field model for the case without sparsity

Optimal Controller and Actuator Design for Nonlinear Parabolic Systems

Many physical systems are modeled by nonlinear parabolic differential equations, such as the Kuramoto-Sivashinsky (KS) equation. In this paper, the existence of a concurrent optimal controller and actuator design is established for semilinear systems. Optimality equations are provided. The results are shown to apply to optimal controller/actuator design for the Kuramoto-Sivashinsky equation and also nonlinear diffusion

Shape Representation in Primate Visual Area 4 and Inferotemporal Cortex

The representation of contour shape is an essential component of object recognition, but the cortical mechanisms underlying it are incompletely understood, leaving it a fundamental open question in neuroscience. Such an understanding would be useful theoretically as well as in developing computer vision and Brain-Computer Interface applications. We ask two fundamental questions: “How is contour shape represented in cortex and how can neural models and computer vision algorithms more closely approximate this?” We begin by analyzing the statistics of contour curvature variation and develop a measure of salience based upon the arc length over which it remains within a constrained range. We create a population of V4-like cells – responsive to a particular local contour conformation located at a specific position on an object’s boundary – and demonstrate high recognition accuracies classifying handwritten digits in the MNIST database and objects in the MPEG-7 Shape Silhouette database. We compare the performance of the cells to the “shape-context” representation (Belongie et al., 2002) and achieve roughly comparable recognition accuracies using a small test set. We analyze the relative contributions of various feature sensitivities to recognition accuracy and robustness to noise. Local curvature appears to be the most informative for shape recognition. We create a population of IT-like cells, which integrate specific information about the 2-D boundary shapes of multiple contour fragments, and evaluate its performance on a set of real images as a function of the V4 cell inputs. We determine the sub-population of cells that are most effective at identifying a particular category. We classify based upon cell population response and obtain very good results. We use the Morris-Lecar neuronal model to more realistically illustrate the previously explored shape representation pathway in V4 – IT. We demonstrate recognition using spatiotemporal patterns within a winnerless competition network with FitzHugh-Nagumo model neurons. Finally, we use the Izhikevich neuronal model to produce an enhanced response in IT, correlated with recognition, via gamma synchronization in V4. Our results support the hypothesis that the response properties of V4 and IT cells, as well as our computer models of them, function as robust shape descriptors in the object recognition process

Analytical and Numerical Approaches to Initiation of Excitation Waves

This thesis studies the problem of initiation of propagation of excitation waves in one- dimensional spatially extended excitable media. In a study which set out to determine an analytical criteria for the threshold conditions, Idris and Biktashev [68] showed that the linear approximation of the (center-)stable manifold of a certain critical solution yields analytical approximation of the threshold curves, separating initial (or boundary) conditions leading to propagation wave solutions from those leading to decay solutions. The aim of this project is to extend this method to address a wider class of ex- citable systems including multicomponent reaction-diffusion systems, systems with non-self-adjoint linearized operators and in particular, systems with moving critical solutions (critical fronts and critical pulses). In the case of one-component excitable systems where the critical solution is the critical nucleus, we also extend the theory to a quadratic approximation for the purpose of improving the accuracy of the linear approximation. The applicability of the approach is tested through five test problems with either traveling front such as Biktashev model, a simplified cardiac excitation model or traveling pulse solutions including Beeler-Reuter model, near realistic cardiac excitation model. Apart from some exceptional cases, it is not always possible to obtain explicit solution for the essential ingredients of the theory due to the nonlinear nature of the problem. Thus, this thesis also covers a hybrid method, where these ingredients are found numerically. Another important finding of the research is the use of the perturbation theory to find the approximate solution of the essential ingredients of FitzHugh-Nagumo system by using the exact analytical solutions of its primitive ver- sion, Zeldovich-Frank-Kamenetsky equation