In 1948, Richard Feynman published the first paper on his new approach to non-relativistic quantum mechanics. Before Feynman's work there were two mathematical formulations of quantum mechanics. Schrödinger's formulation was based on PDE (the Schrödinger equation) and states representation by wave functions, so it was in the framework of analysis and differential equations. The other formulation was Heisenberg's matrix algebra. Initially, they were thought to be competing. The proponents of one claimed that the other was “ wrong. ” Within a couple of years, John von Neumann had proved that they are equivalent. Although Feynman's theory was not fundamentally new, it nonetheless offered an entirely fresh and different perspective: via a precise formulation of Bohr's correspondence principle, it made quantum mechanics similar to classical mechanics in a precise sense. In addition, Feynman's approach made it possible to explain physical experiments, and, via diagrams, link them directly to computations. What resulted was a very powerful device for computing energies and scattering amplitudes - the famous Feynman's diagrams. In his formulation, Feynman aimed at representing the solution to the non-relativistic Schrödinger equation in the form of an “ average ” over histories or paths of a particle. This solution is commonly known as the Feynman path integral. It plays an important role in the theory but appears as a postulate based on intuition coming from physics rather than a justified mathematical object. This is why Feynman's vision has caught the attention of many mathematicians as well as physicists. The papers of Gelfand, Cameron, and Nelson are among the first, and more substantial, attempts to supply Feynman's theory with a rigorous mathematical foundation. These attempts were followed by many others, but unfortunately all of them were not quite satisfactory. The difficulty comes from a need to define a measure on an infinite-dimensional space of continuous functions that represent all possible paths of a particle. This Feynman's measure has to produce an integral with the properties requested by Feynman. In particular, the expression for the Feynman measure has to involve the non-absolutely integrable Fresnel integrands. The non-absolute integrability of the Fresnel integrands makes the measure fail to be positive and to have the countably additive property. Thus, a well-defined measure in the case of the Feynman path integral does not exist. Extensive research has been done on the methods of relating the Feynman path integral to the integral with respect to the Wiener measure. The method of analytic continuation in mass defines the Feynman path integral as a certain limit of the Wiener integrals. Unfortunately, this method can be used as definition for only almost all values of the mass parameter in the Schrödinger equation. For physicists, this is not a satisfactory result and needs to be improved. In this work we examine those questions which originally led to the Feynman path integral. By now we know that Feynman's “ dream ” cannot be realized as a positive and countably additive measure on the path-space. Here, we offer a new way out by modifying Feynman's question, and thereby achieving a solution to the Schrödinger equation via a different kind of averages in the path-space. We give our version of the question that Feynman “ should have asked ” in order to realize the elusive path integral. In our formulation, we get a Feynman path integral as a limit of linear functionals, as opposed to the more familiar inductive limits of positive measures, traditionally used for constructing the Wiener measure, and related Gaussian families. We adapt here an approach pioneered by Patrick Muldowney. In it, Muldowney suggested a Henstock integration technique in order to deal with the non-absolute integrability of the kind of Fresnel integrals which we need in our solution to Feynman's question. By applying Henstock's theory to Fresnel integrals, we construct a complex-valued “ probability distribution functions ” on the path-space. Then we use this “ probability ” distribution function to define the Feynman path integral as an inductive limit. This establishes a mathematically rigorous Feynman limit, and at the same time, preserves Feynman's intuitive idea in resulting functional. In addition, our definition, and our solution, do not place any restrictions on any of the parameters in the Schrödinger equation, and have a potential to offer useful computational experiments, and other theoretical insights.

Probability theory is the study of random phenomena. Many dynamical systems with random influence, in nature or artificial complex systems, are better modeled by equations incorporating the intrinsic stochasticity involved. In probability theory, stochastic partial differential equations (SPDEs) generalize partial differential equations through random force terms and coefficients, while stochastic differential equations (SDEs) generalize ordinary differential equations. They are both abound in models involving Brownian motion throughout science, engineering and economics. However, Brownian motion is just one example of a random noisy input. The goal of this thesis is to make contributions in the study and applications of stochastic dynamical systems involving a wider variety of stochastic processes and noises. This is achieved by considering different models arising out of applications in thermal engineering, population dynamics and mathematical finance.<br><div><br></div><div>1. Power-type non-linearities in SDEs with rough noise: We consider a noisy differential equation driven by a rough noise that could be a fractional Brownian motion, a generalization of Brownian motion, while the equation's coefficient behaves like a power function. These coefficients are interesting because of their relation to classical population dynamics models, while their analysis is particularly challenging because of the intrinsic singularities. Two different methods are used to construct solutions: (i) In the one-dimensional case, a well-known transformation is used; (ii) For multidimensional situations, we find and quantify an improved regularity structure of the solution as it approaches the origin. Our research is the first successful analysis of the system described under a truly rough noise context. We find that the system is well-defined and yields non-unique solutions. In addition, the solutions possess the same roughness as that of the noise.<br></div><div><br></div><div>2. Parabolic Anderson model in rough environment: The parabolic Anderson model is one of the most interesting and challenging SPDEs used to model varied physical phenomena. Its original motivation involved bound states for electrons in crystals with impurities. It also provides a model for the growth of magnetic field in young stars and has an interpretation as a population growth model. The model can be expressed as a stochastic heat equation with additional multiplicative noise. This noise is traditionally a generalized derivative of Brownian motion. Here we consider a one dimensional parabolic Anderson model which is continuous in space and includes a more general rough noise. We first show that the equation admits a solution and that it is unique under some regularity assumptions on the initial condition. In addition, we show that it can be represented using the Feynman-Kac formula, thus providing a connection with the SPDE and a stochastic process, in this case a Brownian motion. The bulk of our study is devoted to explore the large time behavior of the solution, and we provide an explicit formula for the asymptotic behavior of the logarithm of the solution.<br></div><div><br></div><div>3. Heat conduction in semiconductors: Standard heat flow, at a macroscopic level, is modeled by the random erratic movements of Brownian motions starting at the source of heat. However, this diffusive nature of heat flow predicted by Brownian motion is not observed in certain materials (semiconductors, dielectric solids) over short length and time scales. The thermal transport in these materials is more akin to a super-diffusive heat flow, and necessitates the need for processes beyond Brownian motion to capture this heavy tailed behavior. In this context, we propose the use of a well-defined Lévy process, the so-called relativistic stable process to better model the observed phenomenon. This process captures the observed heat dynamics at short length-time scales and is also closely related to the relativistic Schrödinger operator. In addition, it serves as a good candidate for explaining the usual diffusive nature of heat flow under large length-time regimes. The goal is to verify our model against experimental data, retrieve the best parameters of the process and discuss their connections to material thermal properties.<br></div><div><br></div><div>4. Bond-pricing under partial information: We study an information asymmetry problem in a bond market. Especially we derive bond price dynamics of traders with different levels of information. We allow all information processes as well as the short rate to have jumps in their sample paths, thus representing more dramatic movements. In addition we allow the short rate to be modulated by all information processes in addition to having instantaneous feedbacks from the current levels of itself. A fully informed trader observes all information which affects the bond price while a partially informed trader observes only a part of it. We first obtain the bond price dynamic under the full information, and also derive the bond price of the partially informed trader using Bayesian filtering method. The key step is to perform a change of measure so that the dynamic under the new measure becomes computationally efficient.</div>

The most suitable model that idealizes random sequences of shock and impacts on vibratory systems is that of a random train of pulses (or impulses), whose arrivals are characterized in terms of stochastic point processes. Most of the existing methods of stochastic dynamics are relevant to random impulsive excitations driven by Poisson processes and there exist some methods for Erlang renewal-driven impulse processes. Herein, two classes of random impulse processes are considered. The first one is the train of impulses whose interarrival timesare driven by an Erlang renewal process. The second class is obtained by selecting some impulses from the train driven by an Erlang renewal process. The selection is performed with the aid of the jump, zero-one, stochastic process governed by the stochastic differential equation driven by the independent Erlang renewal processes. The underlying counting process, driving the arrival times of the impulses, is fully characterized. The expressions for the probability density functions of the first and second waiting times are derived and by means of these functions it is proved that the underlying counting process is a renewal (non-Erlang) process. The probability density functions of the interarrival times are evaluated for four different cases of the driving process and the results obtained for some example sets of parameters are shown graphically. The advantage of modeling the interarrival times using the class of non-Erlang renewal processes analyzed in the present dissertation, rather than the Poisson or Erlang distributions is that it is possible to deal with a broader class of the interarrival probability density functions. The non-Erlang renewal processes considered herein, obtained from two independent Erlang renewal processes, are characterized by four parameters that can be chosen to fit more closely the actual data on the distribution of the interarrival times. As the renewal counting process is not the one with independent increments, the state vector of the dynamic system under a renewal impulse process excitation is not a Markov process. The non-Markov problem may be then converted into a Markov one at the expense of augmenting the state vector by auxiliary discrete stochastic variables driven by a Poisson process. Other than the existing in literature (Iwankiewicz and Nielsen), a novel technique of conversion is devised here, where the auxiliary variables are all zero-one processes. In a considered class of non-Erlang renewal impulse processes each of the driving Erlang processes is recast in terms of the Poisson process, the augmented state vector driven by two independent Poisson processes becomes a non-diffusive Markov process. For a linear oscillator, under a considered class of non-Erlang renewal impulse process, the equations for response moments are obtained from the generalized Ito’s differential rule and the mean value and variance of the response are evaluated and shown graphically for some selected sets of parameters. For a non-linear oscillator under both Erlang renewal-driven impulses and the considered class of non-Erlang renewal impulse processes, the technique of equations for moments together with a modified closure technique is devised. The specific physical properties of an impulsive load process allow to modify the classical cumulant-neglect closure scheme and to develop a more efficient technique for the class of excitations considered. The joint probability density of the augmented state vector is expressed as sum of contributions conditioned on the ‘on’ and ‘off’ states of the auxiliary variables. A discrete part of the joint probability density function accounts for the fact that there is a finite probability of the system being in a deterministic state (for example at rest) from the initial time to the occurrence of the first impulse. The continuous part, which is the conditional probability given that the first impulse has occurred, can be expressed in terms of functions of the displacement and velocity of the system. These functions can be viewed as unknown probability densities of a bi-variate stochastic process, each of which originates a set of ‘conditional moments’. The set of relationships between unconditional and conditional moments is derived. The ordinary cumulant neglect closure is then performed on the conditional moments pertinent to the continuous part only. The closure scheme is then formulated by expressing the ‘unconditional’ moments of order greater then the order of closure, in terms of unconditional moments of lower order. The stochastic analysis of a Duffing oscillator under the the random train of impulses driven by an Erlang renewal processes and a non-Erlang renewal process R(t), is performed by applying the second order ordinary cumulant neglect closure and the modified second order closure approximation and the approximate analytical results are verified against direct Monte Carlo simulation. The modified closure scheme proves to give better results for highly non-Gaussian train of impulses, characterized by low mean arrival rate.

The organization of molecules within a cell is central to cellular processes ranging from metabolism and damage repair to migration and replication. Uncovering the emergent properties of this biomolecular organization can improve our understanding of how organisms function and reveal ways to repurpose their components outside of the cell. This dissertation focuses on the role of organization in two widely studied systems: enzyme cascades and active cytoskeletal filaments.Part I of this dissertation studies the emergent properties of the spatial organization of enzyme cascades. Enzyme cascades consist of a series of enzymes that catalyze sequential reactions: the product of one enzyme is the substrate of a subsequent enzyme. Enzyme cascades are a fundamental component of cellular reaction pathways, and spatial organization of the cascading enzymes is often essential to their function. For example, cascading enzymes assembled into multi-enzyme complexes can protect unstable cascade intermediates from the environment by forming tunnels between active sites. We use mathematical modeling to investigate the role of spatial organization in three specific systems. First, we examine enzyme cascade reactions occurring in multi-enzyme complexes where active sites are connected by tunnels. Using stochastic simulations and theoretical results from queueing theory, we demonstrate that the fluctuations arising from the small number of molecules involved can cause non-negligible disruptions to cascade throughput. Second, we develop a set of design principles for a compartmentalized cascade reaction with an unstable intermediate and show that there exists a critical kinetics-dependent threshold at which compartments become useful. Third, we investigate enzyme cascades immobilized on a synthetic DNA origami scaffold and show that the scaffold can create a favorable microenvironment for catalysis. Part II of this dissertation focuses on the organization of active cytoskeletal filaments. Many mechanical processes of a cell, such as cell division, cell migration, and intracellular transport, are driven by the ATP-fueled motion of motor proteins (kinesin, dynein, or myosin) along cytoskeletal filaments (microtubules or actin filaments). Over the past two decades, researchers have been repurposing motor protein-driven propulsion outside of the cell to create systems where cytoskeletal filaments glide on surfaces coated with motor proteins. The study of these systems not only elucidates the mechanisms of force production within the cell, but also opens new avenues for applications ranging from molecular detection to computation. We examine how microtubules gliding on surfaces coated with kinesin motor proteins can generate collective behavior in response to mutualistic interactions between the filaments and motors, thereby maximizing the utilization of system components and production. To this end, we used a microtubule-kinesin system where motors reversibly bind to the surface. In experiments, microtubules gliding on these reversibly bound motors were unable to cross each other and at high enough densities began to align and form long, dense bundles. The kinesin motors accumulated in trails surrounding the microtubule bundles and participated in microtubule transport. In conclusion, our study of the emergent properties of the spatial organization of enzyme cascades and the mutualistic interactions within active systems of motor proteins and cytoskeletal filaments provides insight into both how these systems function within cells and how they can be repurposed outside of them.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21080号 / 工博第4444号 / 新制||工||1691(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 KIM Chul-Woo, 教授 杉浦 邦征, 教授 八木 知己 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

This thesis consists of four chapters. In Chapter 1, we focus on a class of transportation inequalities known as the transportation-information inequalities. These inequalities bound optimal transportation costs in terms of relative Fisher information, and are known to characterize certain concentration properties of Markov processes around their invariant measures. We provide a characterization of the quadratic transportation-information inequality in terms of a dimension-free concentration property for i.i.d. copies of the underlying Markov process, identifying the precise high-dimensional concentration property encoded by this inequality. We also illustrate how this result is an instance of a general convex-analytic tensorization principle. In Chapter 2, we study the entropic gradient flow property of McKean--Vlasov diffusions via a stochastic analysis approach. We formulate a trajectorial version of the relative entropy dissipation identity for these interacting diffusions, which describes the rate of relative entropy dissipation along every path of the diffusive motion. As a first application, we obtain a new interpretation of the gradient flow structure for the granular media equation. Secondly, we show how the trajectorial approach leads to a new derivation of the HWBI inequality. In Chapter 3, we further extend the trajectorial approach to a class of degenerate diffusion equations that includes the porous medium equation. These equations are posed on a bounded domain and are subject to no-flux boundary conditions, so that their corresponding probabilistic representations are stochastic differential equations with normal reflection on the boundary. Our stochastic analysis approach again leads to a new derivation of the Wasserstein gradient flow property for these nonlinear diffusions, as well as to a simple proof of the HWI inequality in the present context. Finally, in Chapter 4, we turn our attention to mean field approximation -- a method widely used to study the behavior of large stochastic systems of interacting particles. We propose a new approach to deriving quantitative mean field approximations for any strongly log-concave probability measure. Our framework is inspired by the recent theory of nonlinear large deviations, for which we offer an efficient non-asymptotic perspective in log-concave settings based on functional inequalities. We discuss three implications, in the contexts of continuous Gibbs measures on large graphs, high-dimensional Bayesian linear regression, and the construction of decentralized near-optimizers in high-dimensional stochastic control problems.

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During strong ground shaking structures often become inelastic and respond hysteretically. Therefore, in this study some hysteretic models commonly used in seismic structural analysis are studied. In particular the characteristics of a popular endochronic model proposed by Bouc and Wen are examined in detail. In addition, analytical expressions have also been developed for most commonly used bilinear model as well as another model, herein called as the hyperbolic model.</p> / Master of Science

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