A family of integrable maps associated with the Volterra lattice

Recently Gubbiotti, Joshi, Tran and Viallet classified birational maps in four dimensions admitting two invariants (first integrals) with a particular degree structure, by considering recurrences of fourth order with a certain symmetry. The last three of the maps so obtained were shown to be Liouville integrable, in the sense of admitting a non-degenerate Poisson bracket with two first integrals in involution. Here we show how the first of these three Liouville integrable maps corresponds to genus 2 solutions of the infinite Volterra lattice, being the $g=2$ case of a family of maps associated with the Stieltjes continued fraction expansion of a certain function on a hyperelliptic curve of genus $g\geqslant 1$. The continued fraction method provides explicit Hankel determinant formulae for tau functions of the solutions, together with an algebro-geometric description via a Lax representation for each member of the family, associating it with an algebraic completely integrable system. In particular, in the elliptic case ($g=1$), as a byproduct we obtain Hankel determinant expressions for the solutions of the Somos-5 recurrence, but different to those previously derived by Chang, Hu and Xin. By applying contraction to the Stieltjes fraction, we recover integrable maps associated with Jacobi continued fractions on hyperelliptic curves, that one of us considered previously, as well as the Miura-type transformation between the Volterra and Toda lattices

A family of integrable maps associated with the Volterra lattice

Recently Gubbiotti, Joshi, Tran and Viallet classified birational maps in four dimensions admitting two invariants (first integrals) with a particular degree structure, by considering recurrences of fourth order with a certain symmetry. The last three of the maps so obtained were shown to be Liouville integrable, in the sense of admitting a non-degenerate Poisson bracket with two first integrals in involution. Here we show how the first of these three Liouville integrable maps corresponds to genus 2 solutions of the infinite Volterra lattice, being the g = 2 case of a family of maps associated with the Stieltjes continued fraction expansion of a certain function on a hyperelliptic curve of genus g ⩾ 1. The continued fraction method provides explicit Hankel determinant formulae for tau functions of the solutions, together with an algebro-geometric description via a Lax representation for each member of the family, associating it with an algebraic completely integrable system. In particular, in the elliptic case (g = 1), as a byproduct we obtain Hankel determinant expressions for the solutions of the Somos-5 recurrence, but different to those previously derived by Chang, Hu and Xin. By applying contraction to the Stieltjes fraction, we recover integrable maps associated with Jacobi continued fractions on hyperelliptic curves, that one of us considered previously, as well as the Miura-type transformation between the Volterra and Toda lattices

Studies on the geometry of Painlevé equations

This thesis is a collection of work within the geometric framework for Painlevé equations. This approach was initiated by the Japanese school, and is based on studying Painlevé equations (differential or discrete) via certain rational surfaces associated with affine root systems. Our work is grouped into two main themes: on the one hand making use of tools and techniques from the geometric framework to study problems from applications where Painlevé equations appear, and on the other hand developing and extending the geometric framework itself. Differential and discrete Painlevé equations arise in a wide range of areas of mathematics and physics, and we present a general procedure for solving the identification problem for Painlevé equations. That is, if a differential or discrete system is suspected to be equivalent to one of Painlevé type, we outline a method, based on constructing the associated surfaces explicitly, for identifying the system with a standard example, in which case known results can be used, and demonstrate it in the case of equations appearing in the theory of orthogonal polynomials. Our results on the geometric framework itself begin with an observation of a new class of discrete equations that can described through the geometric theory, beyond those originally defined by Sakai in terms of translation symmetries of families of surfaces. To be precise, we build on previous studies of equations corresponding to non-translation symmetries of infinite order (so-called projective reductions, with fewer parameters than translations of the same surface type) and show that Sakai’s theory allows for integrable discrete equations to be constructed from any element of infinite order in the symmetry group and still have the full parameter freedom for their surface type. We then also make the first steps toward a geometric theory of delay-differential Painlevé equations by giving a description of singularity confinement in this setting in terms of mappings between jet spaces

Modeling economies and ecosystems in general equilibrium

This work exploits the general equilibrium modeling framework to simulate complex systems, an economy and an ecosystem. In an economic application, this work leverages a novel data revision scheme to integrate technological detail on electricity generation and pollution abatement into national accounts data in a traditional economic computed general equilibrium (CGE) model. This integration provides a rich characterization of generation and abatement for multiple fuel sources and pollutants across 72 different generation-abatement technology configurations. Results reveal that the benefits of reductions in oxides of nitrogen and sulfur from a carbon policy in the US electric sector are on the order of $10 bn., which rival the policy's welfare costs and make 12-13% carbon abatement economically justifiable without considering any climate benefits. For ecosystem applications, this work demonstrates how the structure of economic CGE modeling can be adapted to construct a Biological General Equilibrium (BGE) model grounded in the theoretical biology literature. The BGE model contributes a novel synthesis of micro-behavioral, bioenergetic features with macroscopic ecosystem outcomes and empirical food web data. Species respond to prevailing ecosystem scarcity conditions that impinge on their energy budgets driving population outcomes within and across model periods. This adaptive capacity is a critical advance over the commonly-taken phenomenological or first-order parametric approaches. The distinctive design of the BGE model enables numerical examination of how changes in scarcity drives biomass production and consumption in a complex food web. Moreover, the BGE model design can exploit empirical datasets used by extant ecosystem models to offer this level of insight for a wide cast of ecosystems. Monte carlo simulations demonstrate that the BGE framework can produce stable results for the ecosystem robust to a variety of shocks and parameterizations. The BGE model's validity is supported in tests against real-world phenomena within the Aleutian ecosystem - both an invasive species and a harvesting-induced trophic cascade - by mimicking key features of these phenomena. The BGE model's micro-founded dynamics, the stability and robustness of its results, and its validity against real-world phenomena offer a unique and valuable contribution to ecosystem modeling and a way forward for the integrated assessment of human-ecosystem interactions

Mathematical and Numerical Aspects of Dynamical System Analysis

From Preface: This is the fourteenth time when the conference “Dynamical Systems: Theory and Applications” gathers a numerous group of outstanding scientists and engineers, who deal with widely understood problems of theoretical and applied dynamics. Organization of the conference would not have been possible without a great effort of the staff of the Department of Automation, Biomechanics and Mechatronics. The patronage over the conference has been taken by the Committee of Mechanics of the Polish Academy of Sciences and Ministry of Science and Higher Education of Poland. It is a great pleasure that our invitation has been accepted by recording in the history of our conference number of people, including good colleagues and friends as well as a large group of researchers and scientists, who decided to participate in the conference for the first time. With proud and satisfaction we welcomed over 180 persons from 31 countries all over the world. They decided to share the results of their research and many years experiences in a discipline of dynamical systems by submitting many very interesting papers. This year, the DSTA Conference Proceedings were split into three volumes entitled “Dynamical Systems” with respective subtitles: Vibration, Control and Stability of Dynamical Systems; Mathematical and Numerical Aspects of Dynamical System Analysis and Engineering Dynamics and Life Sciences. Additionally, there will be also published two volumes of Springer Proceedings in Mathematics and Statistics entitled “Dynamical Systems in Theoretical Perspective” and “Dynamical Systems in Applications”

Potential-based Formulations of the Navier-Stokes Equations and their Application

Based on a Clebsch-like velocity representation and a combination of classical variational principles for the special cases of ideal and Stokes flow a novel discontinuous Lagrangian is constructed; it bypasses the known problems associated with non-physical solutions and recovers the classical Navier-Stokes equations together with the balance of inner energy in the limit when an emerging characteristic frequency parameter tends to infinity. Additionally, a generalized Clebsch transformation for viscous flow is established for the first time. Next, an exact first integral of the unsteady, three-dimensional, incompressible Navier-Stokes equations is derived; following which gauge freedoms are explored leading to favourable reductions in the complexity of the equation set and number of unknowns, enabling a self-adjoint variational principle for steady viscous flow to be constructed. Concurrently, appropriate commonly occurring physical and auxiliary boundary conditions are prescribed, including establishment of a first integral for the dynamic boundary condition at a free surface. Starting from this new formulation, three classical flow problems are considered, the results obtained being in total agreement with solutions in the open literature. A new least-squares finite element method based on the first integral of the steady two-dimensional, incompressible, Navier-Stokes equations is developed, with optimal convergence rates established theoretically. The method is analysed comprehensively, thoroughly validated and shown to be competitive when compared to a corresponding, standard, primitive-variable, finite element formulation. Implementation details are provided, and the well-known problem of mass conservation addressed and resolved via selective weighting. The attractive positive definiteness of the resulting linear systems enables employment of a customized scalable algebraic multigrid method for efficient error reduction. The solution of several engineering related problems from the fields of lubrication and film flow demonstrate the flexibility and efficiency of the proposed method, including the case of unsteady flow, while revealing new physical insights of interest in their own right

Convex Identifcation of Stable Dynamical Systems

This thesis concerns the scalable application of convex optimization to data-driven modeling of dynamical systems, termed system identi cation in the control community. Two problems commonly arising in system identi cation are model instability (e.g. unreliability of long-term, open-loop predictions), and nonconvexity of quality-of- t criteria, such as simulation error (a.k.a. output error). To address these problems, this thesis presents convex parametrizations of stable dynamical systems, convex quality-of- t criteria, and e cient algorithms to optimize the latter over the former. In particular, this thesis makes extensive use of Lagrangian relaxation, a technique for generating convex approximations to nonconvex optimization problems. Recently, Lagrangian relaxation has been used to approximate simulation error and guarantee nonlinear model stability via semide nite programming (SDP), however, the resulting SDPs have large dimension, limiting their practical utility. The rst contribution of this thesis is a custom interior point algorithm that exploits structure in the problem to signi cantly reduce computational complexity. The new algorithm enables empirical comparisons to established methods including Nonlinear ARX, in which superior generalization to new data is demonstrated. Equipped with this algorithmic machinery, the second contribution of this thesis is the incorporation of model stability constraints into the maximum likelihood framework. Speci - cally, Lagrangian relaxation is combined with the expectation maximization (EM) algorithm to derive tight bounds on the likelihood function, that can be optimized over a convex parametrization of all stable linear dynamical systems. Two di erent formulations are presented, one of which gives higher delity bounds when disturbances (a.k.a. process noise) dominate measurement noise, and vice versa. Finally, identi cation of positive systems is considered. Such systems enjoy substantially simpler stability and performance analysis compared to the general linear time-invariant iv Abstract (LTI) case, and appear frequently in applications where physical constraints imply nonnegativity of the quantities of interest. Lagrangian relaxation is used to derive new convex parametrizations of stable positive systems and quality-of- t criteria, and substantial improvements in accuracy of the identi ed models, compared to existing approaches based on weighted equation error, are demonstrated. Furthermore, the convex parametrizations of stable systems based on linear Lyapunov functions are shown to be amenable to distributed optimization, which is useful for identi cation of large-scale networked dynamical systems