The thesis consists of four papers on numerical complexityanalysis of weak approximation of ordinary and partialstochastic differential equations, including illustrativenumerical examples. Here by numerical complexity we mean thecomputational work needed by a numerical method to solve aproblem with a given accuracy. This notion offers a way tounderstand the efficiency of different numerical methods. The first paper develops new expansions of the weakcomputational error for Itˆo stochastic differentialequations using Malliavin calculus. These expansions have acomputable leading order term in a posteriori form, and arebased on stochastic flows and discrete dual backward problems.Beside this, these expansions lead to efficient and accuratecomputation of error estimates and give the basis for adaptivealgorithms with either deterministic or stochastic time steps.The second paper proves convergence rates of adaptivealgorithms for Itˆo stochastic differential equations. Twoalgorithms based either on stochastic or deterministic timesteps are studied. The analysis of their numerical complexitycombines the error expansions from the first paper and anextension of the convergence results for adaptive algorithmsapproximating deterministic ordinary differential equations.Both adaptive algorithms are proven to stop with an optimalnumber of time steps up to a problem independent factor definedin the algorithm. The third paper extends the techniques to theframework of Itˆo stochastic differential equations ininfinite dimensional spaces, arising in the Heath Jarrow Mortonterm structure model for financial applications in bondmarkets. Error expansions are derived to identify differenterror contributions arising from time and maturitydiscretization, as well as the classical statistical error dueto finite sampling. The last paper studies the approximation of linear ellipticstochastic partial differential equations, describing andanalyzing two numerical methods. The first method generates iidMonte Carlo approximations of the solution by sampling thecoefficients of the equation and using a standard Galerkinfinite elements variational formulation. The second method isbased on a finite dimensional Karhunen- Lo`eve approximation ofthe stochastic coefficients, turning the original stochasticproblem into a high dimensional deterministic parametricelliptic problem. Then, adeterministic Galerkin finite elementmethod, of either h or p version, approximates the stochasticpartial differential equation. The paper concludes by comparingthe numerical complexity of the Monte Carlo method with theparametric finite element method, suggesting intuitiveconditions for an optimal selection of these methods. 2000Mathematics Subject Classification. Primary 65C05, 60H10,60H35, 65C30, 65C20; Secondary 91B28, 91B70. / QC 20100825

This work entails studying unfitted finite element discretizations for convection-diffusion equations in domains that evolve in time. In particular, these partial differential equations model the evolution of the concentration of soluble surfactants in bulk-interface domains. The work in this thesis docuses on developing numerical methods which conserve the modeled physical quantities. In this work, we propose cut finite element discretizations based on the Discontinuous Galerkin framework which are both locally and globally conservative. Local conservation is achieved on so-called macro elements, and we investigate macro element partitioning of the mesh for both stationary and time-dependent domains. Additionally, we develop globally conservative methods for time-dependent problems. We analyze the proposed methods by studying the convergence of the L2-error with respect to mesh size, condition numbers of the associated linear system matrices, and the conservation error. In numerical experiments for time-dependent problems, we show that the proposed methods have optimal convergence and that the developed macro element stabilization for time-dependent problems leads to increased accuracy while retaining stable condition numbers. Moreover, the measured conservation errors verify the global conservation of the proposed methods. / Detta arbete undersöker diskretiseringar av partiella differentialekvationer i tidsberoende domäner där beräkningsnätet inte behöver anpassas till domänens rörelse. I synnerhet betraktar vi partiella differentalekvationer som modellerar koncentrationen av lösliga ytaktiva ämnen, och skurna finita elementmetoder baserade på den Diskontinuerliga Galerkinmetoden som bevarar de modellerade fysikaliska storheterna. I detta arbete föreslås diskretiseringar som är både lokalt och globalt konservativa. Lokal konservering uppnås i så kallade makroelement, och vi undersöker makroelementpartitionering för både stationära och tidsberoende domäner. Även globalt konservativa metoder utvecklas för tidsberoende problem. De föreslagna metoderna analyseras med hjälp av numeriska exempel. Vi studerar konvergensen av L2-felet med avseende på nätstorlek, konditionstalen för de linjära systemmatriserna samt konserveringsfelet. Metoderna uppvisar optimal konvergens och makroelementstabilisering som utvecklas för tidsberoende problem leder till ökad noggrannhet, samtidigt som konditionstalen förblir stabila. Dessutom veritifierar de uppmättta konserveringsfelen den globala konserveringen hos de föreslagna metoderna.

Optimal control is a paradigm for solving optimization problems involving dynamical systems, which are to be controlled. It is able to solve fish harvesting problems, in which we want to optimize harvesting out-take by considering fishing as a control function that acts on the state of the dynamical system, which represents the growth of fish species in the environment. Other modelling aspects of optimal control are defining terminal costs and running costs, e.g. maximizing profit. We keep the terminal condition comparable for a different number of species. It is based on the initial population. By using the optimal control Hamiltonian and Pontryagin’s Maximum Principle we can calculate the optimal state trajectories corresponding to suitable optimal controls. The Hamiltonian is dependent on the state equation and the running costs. We present two approaches of modelling the running costs. An approach that is not directly translatable to the fish harvesting problem, but it leads to a smooth Hamiltonian, which greatly simplifies derivation and computation. The other, which is equivalent to maximizing profit, leads to a non-smooth Hamiltonian. This leads to jump-discontinuous derivatives needed for computation. We propose to regularize the derivatives of the Hamiltonian using suitable smooth functions, such that it is equivalent to regularizing the Hamiltonian directly. We give details for implementing both approaches up to systems of n competing species. After which we go into detail on algorithms and programming structure implemented. Finally, in modest numerical experiments, for one and two species, we show the relation between the optimal control and the terminal costs. But more interestingly, that the smooth Hamiltonian models are inadequate and regularized Hamiltonian models are the preferred choice. Intriguingly, the latter approach results in steady state solution, wherethe control acts as a stabilizer. / Optimal kontroll är ett paradigm för att lösa optimeringsproblem som omfattar dynamiska system som ska kontrolleras. Den kan lösa problem med skörd av fisk där vi vill optimera skörd av fisk genom att betrakta fisket som en kontrollfunktion som verkar på tillståndet i det dynamiska systemet, som representerar tillväxten av fiskarter i miljön. Andra modelleringsaspekter av optimal styrning är att definiera slutkostnader och löpande kostnader, t.ex. maximering av vinsten. Vi håller terminalvillkoret jämförbart för ett antal olika arter. Det baseras på den ursprungliga populationen.Genom att använda Hamiltonianen för optimal styrning och Pontryagins maximiprincip kan vi beräkna de optimala tillståndsbanorna som motsvarar lämpliga optimala styrningar. Hamiltonianen är beroende av tillståndsekvationen och driftskostnaderna. Vi presenterar två metoder för att modellera driftskostnaderna. Ett tillvägagångssätt som inte är direkt överförbart till problemet med skörd av fisk, men som leder till en slät Hamiltonian, vilket förenklar härledning och beräkning avsevärt. Den andra metoden, som är likvärdig med vinstmaximering, leder till en icke slät Hamiltonian. Detta leder till hopp-diskontinuerliga derivator som behövs för beräkningen. Vi föreslår att man reglerar Hamiltonianens derivator med hjälp av lämpliga släta funktioner, så att det är likvärdigt med att reglera Hamiltonianen direkt. Vi ger detaljer för genomförandet av bå-da tillvägagångssätten upp till system med n konkurrerande arter. Därefter går vi in i detalj på algoritmer och den implementerade programmeringsstrukturen. Slutligen visar vi genom numeriska experiment, för en och två arter, sambandet mellan den optimala kontrollen och slutkostnaderna. Men mer intressant är att de släta hamiltoniska modellerna är otillräckliga, vilket ger upphov till att reglerade hamiltoniska modeller är att föredra. Intressant nog resulterar det senare tillvägagångssättet i en stabil lösning, där kontrollen fungerar som en stabilisator.

Loads determination for external stores on fighter aircraft is an important task for manufacturers in ensuring the safe operation of their aircraft. Due to the large number of possible store combinations, wind tunnel tests – the primary approach to obtaining loads data – cannot be performed for all configurations. Instead, supplementary techniques to estimating loads are necessary. One approach is to use information from another store and adapt it, using so-called scaling methods, to the non-tested store. In this thesis, a scaling method combining the results of computational fluid dynamics (CFD) simulations, for both a non-tested and a reference store, with existing wind tunnel data for the reference store, is thoroughly examined for a number of different stores, angles of attack, sideslip angles and Mach numbers. The performance of the proposed scaling method is assessed in relation to currently used scaling methods, using non-parametric and multivariate statistics. The results show no definitive improvement in performance for the proposed scaling method over the current methods. Although the proposed method is slightly more conservative, considerable variability in the estimates and an increased time consumption for scaling leads the author to advise against using the proposed method for scaling aerodynamic loads on external stores. / Lastbestämning för yttre utrustning på stridsflygplan är en viktig uppgift för att tillverkarna ska kunna garantera säkerheten för sina flygplan. Då antalet möjliga utrustningskombinationer är mycket stort, kan inte vindtunneltester – normalt den främsta metoden för att erhålla lastdata – utföras för alla konfigurationer. Således behövs kompletterande metoder för att skatta laster. Ett alternativ är att använda data från en annan utrustning och anpassa den, med hjälp av så kallade skalningsmetoder, till den icke-testade utrustningen. I detta examensarbete behandlas en skalningsmetod som kombinerar resultaten från numeriska strömningsberäkningar – så kallade CFD-simuleringar – för både en testad och en icke-testad utrustning med befintliga vindtunneldata för den testade utrustningen. Metoden undersöks grundligt för ett antal olika utrustningar, anfallsvinklar, sidanblåsningsvinklar och Machtal. Prestandan hos den föreslagna skalningsmetoden utvärderas i relation till nu använda skalningsmetoder, baserat på icke-parametrisk och multivariat statistik. Resultaten visar inga definitiva förbättringar av prestanda för den föreslagna skalningsmetoden jämfört med de nuvarande metoderna. Även om den föreslagna metoden är något mer konservativ, så föranleder betydande variationer i skattningar och en ökad tidsåtgång för skalning författaren att avråda från att använda den föreslagna metoden för skalning av luftlaster på yttre utrustning.

In many fields of science, engineering, and economics large amounts of data are stored and there is a need to analyze these data in order to extract information for various purposes. Data mining is a general concept involving different tools for performing this kind of analysis. The development of mathematical models and efficient algorithms is of key importance. In this thesis we discuss algorithms for the reduced rank regression problem and algorithms for the computation of the best multilinear rank approximation of tensors. The first two papers deal with the reduced rank regression problem, which is encountered in the field of state-space subspace system identification. More specifically the problem is \[ \min_{\rank(X) = k} \det (B - X A)(B - X A)\tp, \] where $A$ and $B$ are given matrices and we want to find $X$ under a certain rank condition that minimizes the determinant. This problem is not properly stated since it involves implicit assumptions on $A$ and $B$ so that $(B - X A)(B - X A)\tp$ is never singular. This deficiency of the determinant criterion is fixed by generalizing the minimization criterion to rank reduction and volume minimization of the objective matrix. The volume of a matrix is defined as the product of its nonzero singular values. We give an algorithm that solves the generalized problem and identify properties of the input and output signals causing a singular objective matrix. Classification problems occur in many applications. The task is to determine the label or class of an unknown object. The third paper concerns with classification of handwritten digits in the context of tensors or multidimensional data arrays. Tensor and multilinear algebra is an area that attracts more and more attention because of the multidimensional structure of the collected data in various applications. Two classification algorithms are given based on the higher order singular value decomposition (HOSVD). The main algorithm makes a data reduction using HOSVD of 98--99 \% prior the construction of the class models. The models are computed as a set of orthonormal bases spanning the dominant subspaces for the different classes. An unknown digit is expressed as a linear combination of the basis vectors. The resulting algorithm achieves 5\% in classification error with fairly low amount of computations. The remaining two papers discuss computational methods for the best multilinear rank approximation problem \[ \min_{\cB} \| \cA - \cB\| \] where $\cA$ is a given tensor and we seek the best low multilinear rank approximation tensor $\cB$. This is a generalization of the best low rank matrix approximation problem. It is well known that for matrices the solution is given by truncating the singular values in the singular value decomposition (SVD) of the matrix. But for tensors in general the truncated HOSVD does not give an optimal approximation. For example, a third order tensor $\cB \in \RR^{I \x J \x K}$ with rank$(\cB) = (r_1,r_2,r_3)$ can be written as the product \[ \cB = \tml{X,Y,Z}{\cC}, \qquad b_{ijk}=\sum_{\lambda,\mu,\nu} x_{i\lambda} y_{j\mu} z_{k\nu} c_{\lambda\mu\nu}, \] where $\cC \in \RR^{r_1 \x r_2 \x r_3}$ and $X \in \RR^{I \times r_1}$, $Y \in \RR^{J \times r_2}$, and $Z \in \RR^{K \times r_3}$ are matrices of full column rank. Since it is no restriction to assume that $X$, $Y$, and $Z$ have orthonormal columns and due to these constraints, the approximation problem can be considered as a nonlinear optimization problem defined on a product of Grassmann manifolds. We introduce novel techniques for multilinear algebraic manipulations enabling means for theoretical analysis and algorithmic implementation. These techniques are used to solve the approximation problem using Newton and Quasi-Newton methods specifically adapted to operate on products of Grassmann manifolds. The presented algorithms are suited for small, large and sparse problems and, when applied on difficult problems, they clearly outperform alternating least squares methods, which are standard in the field.

In this report, we study how finite differences can be used to simulate acoustic wave propagation originating from a point source in the ocean using the Helmholtz equation. How to model sediment layers and the vast size of the ocean is studied in particular. The finite differences are implemented with summation by parts operators with boundary conditions enforced with simultaneous approximation terms and projection. The numerical solver is combined with the WaveHoltz method to improve the performance. Sediment layers are handled with interface conditions and the domain is artificially expanded using absorbing layers. The absorbing layer is implemented with an alternative approach to the super-grid method where the domain expansion is accomplished by altering the wave speed rather than with coordinate transformations. To isolate these issues, other parameters such as variations in the ocean floor are neglected. With this simplification, cylindrical coordinates are used and the angular variation is assumed to be zero. This reduces the problem to a quasi-three-dimensional system. We study how the parameters of the alternative absorbing layer approach affect its quality. The numerical solver is verified on several test cases and appears to work according to theory. Finally, a semi-realistic simulation is carried out and the solution seems correct in this setting.