We consider electron dynamics in strong electromagnetic fields, such as those expected from the next generation of high-intensity laser facilities. Beginning with a review of constant classical fields, we demonstrate that the electron motion (as given by the Lorentz force equation) can be divided into one of four Lorentz invariant cases. Parameterising the field tensor in terms of a null tetrad, we calculate the radiative energy spectrum for an electron in crossed fields. Progressing to an infinite plane wave, we demonstrate how the electron orbit in the average rest frame changes from figure-of-eight to circular as the polarisation changes from linear to circular. To move beyond a plane wave one must resort to numerics. We therefore present a novel numerical formulation for solving the Lorentz equation. Our scheme is manifestly covariant and valid for arbitrary electromagnetic field configurations. Finally, we reconsider the case of an infinite plane wave from a strong field QED perspective. At high intensities we predict a substantial redshift of the usual kinematic Compton edge of the photon emission spectrum, caused by the large, intensity dependent effective mass of the electrons inside the laser beam. In addition, we find that the notion of a centre-of-mass frame for a given harmonic becomes intensity dependent.

The common envelope (CE) interaction is a still poorly understood, yet critical phase of evolution in binary systems that is responsible for various astrophysical classes and phenomena. In this thesis, we use various approaches and techniques to investigate different aspects of this interaction, and compare our models to observations. We start with a semi-empirical analysis of post-CE systems to predict the outcome of a CE interaction. Using detailed stellar evolutionary models, we revise the α equation and calculate the ejection efficiency, α, both from observations and simulations consistently. We find a possible anti-correlation between α and the secondary-to- primary mass ratio, suggesting that the response of the donor star might be important for the envelope ejection. Secondly, we present a survey of three-dimensional hydrodynamical simulations of the CE evolution using two different numerical techniques, and find very good agreement overall. However, most of the envelope of the donor is still bound at the end of the simulations and the final orbital separations are larger than the ones of young observed post-CE systems. Despite these two investigations, questions remain about the nature of the extra mechanism required to eject the envelope. In order to study the dynamical response of the donor, we perform one-dimensional stellar evolution simulations of stars evolving with mass loss rates from 0.001 up to a few M⊙/yr. For mass-losing giant stars, the evolution is dynamical and not adiabatic, and we find no significant radius increase in any case. Finally, we investigate whether the substellar companions recently observed in close orbits around evolved stars could have survived the CE interaction, and whether they might have been more massive prior to their engulfment. Using an analytical prescription for the disruption of gravitationally bound objects by ram pressure stripping, we find that the Earth-mass planets around KIC 05807616 could be the remnants of a Jovian-mass planet, and that the other substellar objects are unlikely to have lost significant mass during the CE interaction. / Graduate

Designing Cyber-Physical Systems is hard. Physical testing can be slow, expensive and dangerous. Furthermore computational components make testing all possible behavior unfeasible. Model-based design mitigates these issues by making it possible to iterate over a design much faster. Traditional simulation tools can produce useful results, but their results are traditionally approximations that make it impossible to distinguish a useful simulation from one dominated by numerical error. Verification tools require skills in formal specification and a priori understanding of the particular dynamical system being studied. This thesis presents rigorous simulation, an approach to simulation that uses validated numerics to produce results that quantify and bound all approximation errors accumulated during simulation. This makes it possible for the user to objectively and reliably distinguish accurate simulations from ones that do not provide enough information to be useful. Explicitly quantifying the error in the output has the side-effect of leading to a tool for dealing with inputs that come with quantified uncertainty. We formalize the approach as an operational semantics for a core subset of the domain-specific language Acumen. The operational semantics is extended to a larger subset through a translation. Preliminary results toward proving the soundness of the operational semantics with respect to a denotational semantics are presented. A modeling environment with a rigorous simulator based on the operational semantics is described. The implementation is portable, and its source code is freely available. The accuracy of the simulator on different kinds of systems is explored through a set of benchmark models that exercise different aspects of a rigorous simulator. A case study from the automotive domain is used to evaluate the applicability of the simulator and its modeling language. In the case study, the simulator is used to compute rigorous bounds on the output of a model.

This thesis begins with the fundamentals of conformal field theory in three dimensions. The general properties of the conformal bootstrap are then reviewed. The three-dimensional Ising model is presented from the perspective of the renormalization group, after which the conformal field theory aspect at the critical point is discussed. Finally, the bootstrap programme is applied to the three-dimensional Ising model using numerical techniques, and the results analysed.

Asian options are exotic financial derivative products which price must be calculated by numerical evaluation. In this thesis, we study certain ways of solving partial differential equations, which are associated with these derivatives. Since standard numerical techniques for Asian options are often incorrect and impractical, we discuss their variations, which are efficiently applicable for handling frequent numerical instabilities reflected in form of oscillatory solutions. We will show that this crucial problem can be treated and eliminated by adopting flux limiting techniques, which are total variation dimishing.

Numerical simulation of noise production and propagation is a very complex problem. A methodology fitting for one particular problem can fail for another one. So there are no general guidelines on how to deal with such phenomena. In the present work, noise propagated in non-uniform mean-flow is considered. For most cases, in the propagation field, there is a rather significant region where the mean flow is not uniform, but the sound production is negligible compared to the noise emitted by the source region. In this<p>nearfield, a linear set of propagation equations may be considered (LEE). For such problems, the following simulation methodology is proposed:<p>1. Incompressible/compressible LES simulation in the source region.<p>2. Linearized Euler Equations to propagate the noise through the nonlinear mean flow.<p>3. Kirchhoff method in the farfield, if necessary.<p>This thesis deals with the second item of this system (LEE), including interfacing with the other two steps. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

Epidemics have shaped, sometimes more than wars and natural disasters, demo- graphic aspects of human populations around the world, their health habits and their economies. Ebola and the Middle East Respiratory Syndrome (MERS) are clear and current examples of potential hazards at planetary scale. During the spread of an epidemic disease, there are phenomena, like the sudden extinction of the epidemic, that can not be captured by deterministic models. As a consequence, stochastic models have been proposed during the last decades. A typical forward problem in the stochastic setting could be the approximation of the expected number of infected individuals found in one month from now. On the other hand, a typical inverse problem could be, given a discretely observed set of epidemiological data, infer the transmission rate of the epidemic or its basic reproduction number. Markovian epidemic models are stochastic models belonging to a wide class of pure jump processes known as Stochastic Reaction Networks (SRNs), that are intended to describe the time evolution of interacting particle systems where one particle interacts with the others through a finite set of reaction channels. SRNs have been mainly developed to model biochemical reactions but they also have applications in neural networks, virus kinetics, and dynamics of social networks, among others. 4 This PhD thesis is focused on novel fast simulation algorithms and statistical inference methods for SRNs. Our novel Multi-level Monte Carlo (MLMC) hybrid simulation algorithms provide accurate estimates of expected values of a given observable of SRNs at a prescribed final time. They are designed to control the global approximation error up to a user-selected accuracy and up to a certain confidence level, and with near optimal computational work. We also present novel dual-weighted residual expansions for fast estimation of weak and strong errors arising from the MLMC methodology. Regarding the statistical inference aspect, we first mention an innovative multi- scale approach, where we introduce a deterministic systematic way of using up-scaled likelihoods for parameter estimation while the statistical fittings are done in the base model through the use of the Master Equation. In a di↵erent approach, we derive a new forward-reverse representation for simulating stochastic bridges between con- secutive observations. This allows us to use the well-known EM Algorithm to infer the reaction rates. The forward-reverse methodology is boosted by an initial phase where, using multi-scale approximation techniques, we provide initial values for the EM Algorithm.

Detonation is a supersonic mode of combustion that is modeled by a system of conservation laws of compressible fluid mechanics coupled with the equations describing thermodynamic and chemical properties of the fluid. Mathematically, these governing equations admit steady-state travelling-wave solutions consisting of a leading shock wave followed by a reaction zone. However, such solutions are often unstable to perturbations and rarely observed in laboratory experiments. The goal of this work is to study the stability of travelling-wave solutions of detonation models by the following novel approach. We linearize the governing equations about a base travelling-wave solution and solve the resultant linearized problem using high-order numerical methods. The results of these computations are postprocessed using dynamic mode decomposition to extract growth rates and frequencies of the perturbations and predict stability of travelling-wave solutions to infinitesimal perturbations. We apply this approach to two models based on the reactive Euler equations for perfect gases. For the first model with a one-step reaction mechanism, we find agreement of our results with the results of normal-mode analysis. For the second model with a two-step mechanism, we find that both types of admissible travelling-wave solutions exhibit the same stability spectra. Then we investigate the Fickett’s detonation analogue coupled with a particular reaction-rate expression. In addition to the linear stability analysis of this model, we demonstrate that it exhibits rich nonlinear dynamics with multiple bifurcations and chaotic behavior.

This thesis considers classical methods to solve stochastic control problems and valuation problems from financial mathematics numerically. To this end, (linear) partial differential equations (PDEs) in non-divergence form or the optimality conditions known as the (nonlinear) Hamilton-Jacobi-Bellman (HJB) equations are solved by means of finite differences, volumes and elements. We consider all of these three approaches in detail after a thorough introduction to stochastic control problems and discuss various solution terms including classical solutions, strong solutions, weak solutions and viscosity solutions. A particular role in this thesis play degenerate problems. Here, a new model for the optimal control of an energy storage facility is developed which extends the model introduced in [Chen, Forsyth (2007)]. This four-dimensional HJB equation is solved by the classical finite difference Kushner-Dupuis scheme [Kushner, Dupuis (2001)] and a semi-Lagrangian variant which are both discussed in detail. Additionally, a convergence proof of the standard scheme in the setting of parabolic HJB equations is given. Finite volume schemes are another classical method to solve partial differential equations numerically. Sharing similarities to both finite difference and finite element schemes we develop a vertex-centered dual finite volume scheme. We discuss convergence properties and apply the scheme to the solution of HJB equations, which has not been done in such a broad context, to the best of our knowledge. Astonishingly, this is one of the first times the finite volume approach is systematically discussed for the solution of HJB equations. Furthermore, we give many examples which show advantages and disadvantages of the approach. Finally, we investigate novel tailored non-conforming finite element approximations of second-order PDEs in non-divergence form, utilizing finite-element Hessian recovery strategies to approximate second derivatives in the equation. We study approximations with both continuous and discontinuous trial functions. Of particular interest are a-priori and a-posteriori error estimates as well as adaptive finite element methods. In numerical experiments our method is compared with other approaches known from the literature. We discuss implementations of all three approaches in MATLAB (finite differences and volumes) and FEniCS (finite elements) publicly available in GitHub repositories under https://github.com/janblechschmidt. Many numerical experiments show convergence properties as well as pros and cons of the respective approach. Additionally, a new postprocessing procedure for policies obtained from numerical solutions of HJB equations is developed which improves the accuracy of control laws and their incurred values.

We construct a mathematical model of an order driven market where traders can submit limit orders and market orders to buy and sell securities. We adapt the notion of no free lunch of Harrison and Kreps and Jouini and Kallal to our setting and we prove a no-arbitrage theorem for the model of the order driven market. Furthermore, we compute signatures of order books of different financial markets. Signatures, i.e. the full sequence of definite iterated integrals of a path, are one of the fundamental elements of the theory of rough paths. The theory of rough paths provides a framework to describe the evolution of dynamical systems that are driven by rough signals, including rough paths based on Brownian motion and fractional Brownian motion (see the work of Lyons). We show how we can obtain the solution of a polynomial differential equation and its (truncated) signature from the signature of the driving signal and the initial value. We also present and analyse an ODE method for the numerical solution of rough differential equations. We derive error estimates and we prove that it achieves the same rate of convergence as the corresponding higher order Euler schemes studied by Davie and Friz and Victoir. At the same time, it enhances stability. The method has been implemented for the case of polynomial vector fields as part of the CoRoPa software package which is available at http://coropa.sourceforge.net. We describe both the algorithm and the implementation and we show by giving examples how it can be used to compute the pathwise solution of stochastic rough differential equations driven by Brownian rough paths and fractional Brownian rough paths.