SpringerBriefs on PDEs and Data Science – serie

This book introduces an interesting and alternative way to design absorbing boundary conditions (ABCs) for quantum wave equations, basically the nonlinear Schrödinger equation. The focus of this book is the application of the phase space filter approach to derive accurate radiation conditions for Schrödinger equations.Researchers who are interested in partial differential equations and mathematical physics might find this book appealing.

This book is the first organized collection of some results that have been obtained by the authors, their collaborators, and other researchers in the variational approach to structured deformations. It sets the basis and makes more accessible the theoretical apparatus for assigning an energy to a structured deformation, thereby providing motivation to researchers in applied mathematics, continuum mechanics, engineering, and materials science to study the deformation of a solid body without committing at the outset to a specific mechanical theory. Researchers will benefit from an approach in which elastic, plastic, and fracture phenomena can be treated in a unified way.​The book is intended for an audience acquainted with measure theory, the theory of functions of bounded variation, and continuum mechanics. Any students in their last years of undergraduate studies, graduate students, and researchers with a background in applied mathematics, thecalculus of variations, and continuum mechanics will have the prerequisite to read this book.

This book is on the construction and convergence analysis of implementable algorithms to approximate the optimal control of a stochastic linear-quadratic optimal control problem (SLQ problem, for short) subject to a stochastic PDE. If compared to finite dimensional stochastic control theory, the increased complexity due to high-dimensionality requires new numerical concepts to approximate SLQ problems; likewise, well-established discretization and numerical optimization strategies from infinite dimensional deterministic control theory need fundamental changes to properly address the optimality system, where to approximate the solution of a backward stochastic PDE is conceptually new. The linear-quadratic structure of SLQ problems allows two equivalent analytical approaches to characterize its minimum: ‘open loop’ is based on Pontryagin’s maximum principle, and ‘closed loop’ utilizes the stochastic Riccati equation in combination with the feedback control law. The authors will discuss why, in general, complexities of related numerical schemes differ drastically, and when which direction should be given preference from an algorithmic viewpoint.

PDE constrained optimization problems are frequently encountered in many academic or industrial contexts. This book shows how to attack and solve a given PDE constrained optimization problem thanks to the efficient and expert tool FreeFEM.This book is intended for students and researchers aiming at learning how to efficiently solve, with the software FreeFEM, optimization problems under partial differential equations (PDE) constraints. The readers are expected to have a basic knowledge in the analysis and numerical solving of partial differential equations with finite element methods, in optimization, in algorithmics and numerical implementation.All the codes are available at FreeFEM’s github and can hopefully serve as templates for the users.

This book provides a comprehensive and systematic introduction to inverse problems for stochastic partial differential equations (SPDEs), with particular emphasis on stochastic parabolic and hyperbolic equations. It addresses both the unique challenges and new opportunities that arise in the stochastic setting. Key topics include inverse state problems (such as determining unknown initial conditions) and inverse source problems (identifying unknown source terms), with a focus on the mathematical tools essential for their analysis, especially global Carleman estimates tailored to SPDEs. The book explores fundamental issues of uniqueness, stability, and reconstruction under various measurement scenarios, including internal, boundary, and terminal observations. It highlights how stochasticity can fundamentally alter the nature of inverse problems, sometimes enabling solutions where deterministic approaches fail. Reconstruction methods such as Tikhonov regularization are also discussed in detail. This book is intended for graduate students and researchers in applied mathematics, stochastic analysis, and PDEs, as well as practitioners in fields like mathematical finance, physics, and engineering who require rigorous methods for uncertainty quantification. A moderate background in PDEs, functional analysis, and basic stochastic calculus is beneficial.

This book characterizes the open-loop and closed-loop solvability for time-delayed linear quadratic optimal control problems.  Different from the existing literature, in the current book, we present a theory of deterministic LQ problems with delays which has several new features:Our system is time-varying, with both the state equation and cost functional being allowed to include discrete and distributed delays, both in the state and the control. We take different approaches to discuss the unboundedness of the control operator.The open-loop solvability of the lifted problem is characterized by the solvability of a system of forward-backward integral evolution equations and the convexity condition of the cost functional. Surprisingly, the adjoint equations involve some coupled partial differential equations, which is significantly different from that in the literature, where, the adjoint equations are all some anticipated backward ordinary differential equations.The closed-loop solvability is characterized by the solvability of three equivalent integral operator-valued Riccati equations and two equivalent backward integral evolution equations which are much easier to handle than the differential operator-valued Riccati equations used in the literature to study similar problems.The closed-loop representation of open-loop optimal control is presented through three equivalent integral operator-valued Riccati equations.

This book introduces the concepts of identifiability and observability in mathematical epidemiology, as well as those of observers’ constructions. It first exposes and illustrates on several examples the mathematical definitions and properties of observability and identifiability. A chapter is dedicated to the well-known Kermack McKendrick model, for which the complete analysis of identifiability and observability is not available in the literature. Then, several techniques of observer constructions, in view of online estimation of state and parameters, are presented and deployed on several models. New developments relevant for applications in epidemiology are also given. Finally, practical considerations are discussed with data and numerical simulations related to models previously analysed in the book.The book will be appealing to epidemiological modellers and mathematicians working on models in epidemiology.This book contributes to Sustainable Development Goal 3 (SDG3): Good Health and Well Being.

This book is the result of various master and summer school courses the author has taught. The objective is to provide the reader with an introduction to control theory and to the main tools allowing to treat general control systems. The author hopes this book will serve as motivation to go deeper into the theory or numerical aspects that are not covered in this book. This book might be helpful for graduate students and researchers in the field of control theory.

This book provides a general treatment of a class of functionals modelled on convolution energies with kernel having finite p-moments. A general asymptotic analysis of such non-local functionals is performed, via Gamma-convergence, in order to show that the limit may be a local functional representable as an integral. Energies of this form are encountered in many different contexts and the interest in building up a general theory is also motivated by the multiple interests in applications (e.g. peridynamics theory, population dynamics phenomena and data science). The results obtained are applied to periodic and stochastic homogenization, perforated domains, gradient flows, and point-clouds models.This book is mainly intended for mathematical analysts and applied mathematicians who are also interested in exploring further applications of the theory to pass from a non-local to a local description, both in static problems and in dynamic problems.

This book concerns the theory of optimal transport (OT) and its applications to solving problems in geometric optics. It is a self-contained presentation including a detailed analysis of the Monge problem, the Monge-Kantorovich problem, the transshipment problem, and the network flow problem. A chapter on Monge-Ampère measures is included containing also exercises. A detailed analysis of the Wasserstein metric is also carried out. For the applications to optics, the book describes the necessary background concerning light refraction, solving both far-field and near-field refraction problems, and indicates lines of current research in this area. Researchers in the fields of mathematical analysis, optimal transport, partial differential equations (PDEs), optimization, and optics will find this book valuable. It is also suitable for graduate students studying mathematics, physics, and engineering. The prerequisites for this book include a solid understanding of measure theory and integration, as well as basic knowledge of functional analysis.