Numerical Simulation of Stochastic Di erential Equations

Stochastic differential equation (SDE) is a differential equation in which some of the terms and its solution are stochastic processes. SDEs play a central role in modeling physical systems like finance, Biology, Engineering, to mention some. In modeling process, the computation of the trajectories (sample paths) of solutions to SDEs is very important. However, the exact solution to a SDE is generally difficult to obtain due to non-differentiability character of realizations of the Brownian motion. There exist approximation methods of solutions of SDE. The solutions will be continuous stochastic processes that represent diffusive dynamics, a common modeling assumption for financial, Biology, physical, environmental systems. This Masters' thesis is an introduction and survey of numerical solution methods for stochastic differential equations. Standard numerical methods, local linearization methods and filtering methods are well described. We compute the root mean square errors for each method from which we propose a better numerical scheme. Stochastic differential equations can be formulated from a given ordinary differential equations. In this thesis, we describe two kind of formulations: parametric and non-parametric techniques. The formulation is based on epidemiological SEIR model. This methods have a tendency of increasing parameters in the constructed SDEs, hence, it requires more data. We compare the two techniques numerically.

Determining vehicle drivetrain dynamics using system identification

Target of this book is to propose an approach for modelling drivetrain dynamics in order to design further a vibration control system of a hybrid bus. In this thesis two approaches are examined and compared. First model is obtained by theoretical means: drivetrain is represented as a system of rotating masses, which motion is described with differential equations. Second model is obtained using system identification method: mathematical description of the dynamic behavior of a system is formed based on measured input (torque) and output (speed) data. Then two models are compared and an optimal approach is suggested.

Bayesian analysis of SEIR epidemic models

This thesis concerns the analysis of epidemic models. We adopt the Bayesian paradigm and develop suitable Markov Chain Monte Carlo (MCMC) algorithms. This is done by considering an Ebola outbreak in the Democratic Republic of Congo, former Zaïre, 1995 as a case of SEIR epidemic models. We model the Ebola epidemic deterministically using ODEs and stochastically through SDEs to take into account a possible bias in each compartment. Since the model has unknown parameters, we use different methods to estimate them such as least squares, maximum likelihood and MCMC. The motivation behind choosing MCMC over other existing methods in this thesis is that it has the ability to tackle complicated nonlinear problems with large number of parameters. First, in a deterministic Ebola model, we compute the likelihood function by sum of square of residuals method and estimate parameters using the LSQ and MCMC methods. We sample parameters and then use them to calculate the basic reproduction number and to study the disease-free equilibrium. From the sampled chain from the posterior, we test the convergence diagnostic and confirm the viability of the model. The results show that the Ebola model fits the observed onset data with high precision, and all the unknown model parameters are well identified. Second, we convert the ODE model into a SDE Ebola model. We compute the likelihood function using extended Kalman filter (EKF) and estimate parameters again. The motivation of using the SDE formulation here is to consider the impact of modelling errors. Moreover, the EKF approach allows us to formulate a filtered likelihood for the parameters of such a stochastic model. We use the MCMC procedure to attain the posterior distributions of the parameters of the SDE Ebola model drift and diffusion parts. In this thesis, we analyse two cases: (1) the model error covariance matrix of the dynamic noise is close to zero , i.e. only small stochasticity added into the model. The results are then similar to the ones got from deterministic Ebola model, even if methods of computing the likelihood function are different (2) the model error covariance matrix is different from zero, i.e. a considerable stochasticity is introduced into the Ebola model. This accounts for the situation where we would know that the model is not exact. As a results, we obtain parameter posteriors with larger variances. Consequently, the model predictions then show larger uncertainties, in accordance with the assumption of an incomplete model.

Neopentyyliglykolin ja propionihapon välisen esteröintireaktion kinetiikka

Glykolien esterit ovat haluttuja pintareaktiivisia aineita. Niitä voidaan valmistaa esteröintireaktiolla karboksyylihappojen kanssa katalyytin läsnä ollessa, jolloin toivottu reaktiotuote on yleensä muodostuva monoesteri. Monoesterin saannon lisäämiseksi reaktiossa muodostuvaa vettä voidaan poistaa jatkuvasti reaktiosta. Reaktion tasapainotilan tutkiminen on kuitenkin tärkeää, jotta reaktion kinetiikka tunnettaisiin mahdollisimman hyvin. Tällöin reaktiotuotteita ei poisteta reaktioseoksesta reaktion aikana. Glykolit esteröityvät happojen kanssa kahdessa vaiheessa. Ensimmäisessä vaiheessa muodostuu monoesteriä ja vettä ja toisessa vaiheessa diesteriä ja vettä. Kokeiden perusteella ensimmäinen vaihe on selvästi toista vaihetta nopeampi reaktio. Kirjallisuudessa on esitetty myös kaksi sivureaktiota, transesteröityminen ja disproportionaatio. Reaktion kinetiikka voidaan kuvata ilman näitä pieniä sivureaktiota, mutta täydellisen kuvaamisen vuoksi on ne myös otettava huomioon. Reaktion kinetiikan tutkimiseksi suoritettiin viisi laboratoriokoetta eri lämpötiloissa neopentyyliglykolilla ja propionihapolla homogeenisen para-tolueenisulfonihapon toimiessa katalyyttina. Lähtöaineiden ja tuotteiden konsentraatioita seurattiin ajan funktiona ja saatujen tulosten perusteella sovitettiin reaktiomekanismin differentiaaliyhtälöiden reaktionopeusvakiot. Nopeusvakioiden lämpötilariippuvuutta tutkittiin Arrheniuksen yhtälön avulla. Lisäksi määritettiin tasapainovakiot kullekin osareaktiolle.