54 research outputs found
Simulation of stochastic auto-oscillating systems through variable stepsize algorithms with small noise
The paper considers some questions of the numerical analysis of stochastic auto-oscillating systems and their simulation on computers. A low computer costs, variable stepsize algorithm based on local error estimation of stochastic Runge-Kutta- Fehlberg methods is stated for solving nonlinear stochastic differential equations. In particular, it turns out to be very efficient for dynamical systems with small noise intensity. Results of numerical experiments for a plenty of well-known examples from Physics, Chemistry, Biology and Ecology are illustrated with the help of the dialogue system 'Dynamics and Control'
Effective simulation techniques for biological systems
In this paper we give an overview of some very recent work on the stochastic simulation of systems involving chemical reactions. In many biological systems (such as genetic regulation and cellular dynamics) there is a mix between small numbers of key regulatory proteins, and medium and large numbers of molecules. In addition, it is important to be able to follow the trajectories of individual molecules by taking proper account of the randomness inherent in such a system. We describe different types of simulation techniques (including the stochastic simulation algorithm, Poisson Runge-Kutta methods and the Balanced Euler method) for treating simulations in the three different reaction regimes: slow, medium and fast. We then review some recent techniques on the treatment of coupled slow and fast reactions for stochastic chemical kinetics and discuss how novel computing implementations can enhance the performance of these simulations
Simulation methods with extended stability for stiff biochemical Kinetics
Background: With increasing computer power, simulating the dynamics of complex systems in chemistry and biology is becoming increasingly routine. The modelling of individual reactions in (bio)chemical systems involves a large number of random events that can be simulated by the stochastic simulation algorithm (SSA). The key quantity is the step size, or waiting time, τ, whose value inversely depends on the size of the propensities of the different channel reactions and which needs to be re-evaluated after every firing event. Such a discrete event simulation may be extremely expensive, in particular for stiff systems where τ can be very short due to the fast kinetics of some of the channel reactions. Several alternative methods have been put forward to increase the integration step size. The so-called τ-leap approach takes a larger step size by allowing all the reactions to fire, from a Poisson or Binomial distribution, within that step. Although the expected value for the different species in the reactive system is maintained with respect to more precise methods, the variance at steady state can suffer from large errors as τ grows.Results: In this paper we extend Poisson τ-leap methods to a general class of Runge-Kutta (RK) τ-leap methods. We show that with the proper selection of the coefficients, the variance of the extended τ-leap can be well-behaved, leading to significantly larger step sizes.Conclusions: The benefit of adapting the extended method to the use of RK frameworks is clear in terms of speed of calculation, as the number of evaluations of the Poisson distribution is still one set per time step, as in the original τ-leap method. The approach paves the way to explore new multiscale methods to simulate (bio)chemical systems
Stabilized multilevel Monte Carlo method for stiff stochastic differential equations
A multilevel Monte Carlo (MLMC) method for mean square stable stochastic differential equations with multiple scales is proposed. For such problems, that we call stiff, the performance of MLMC methods based on classical explicit methods deteriorates because of the time step restriction to resolve the fastest scales that prevents to exploit all the levels of the MLMC approach. We show that by switching to explicit stabilized stochastic methods and balancing the stabilization procedure simultaneously with the hierarchical sampling strategy of MLMC methods, the computational cost for stiff systems is significantly reduced, while keeping the computational algorithm fully explicit and easy to implement. Numerical experiments on linear and nonlinear stochastic differential equations and on a stochastic partial differential equation illustrate the performance of the stabilized MLMC method and corroborate our theoretical findings. (C) 2013 Elsevier Inc. All rights reserved
Noise induced changes to dynamic behaviour of stochastic delay differential equations
This thesis is concerned with changes in the behaviour of solutions to parameter-dependent stochastic delay differential equations
Inference and parameter estimation for diffusion processes
Diffusion processes provide a natural way of modelling a variety of physical and economic
phenomena. It is often the case that one is unable to observe a diffusion process
directly, and must instead rely on noisy observations that are discretely spaced in time.
Given these discrete, noisy observations, one is faced with the task of inferring properties
of the underlying diffusion process. For example, one might be interested in
inferring the current state of the process given observations up to the present time (this
is known as the filtering problem). Alternatively, one might wish to infer parameters
governing the time evolution the diffusion process.
In general, one cannot apply Bayes’ theorem directly, since the transition density
of a general nonlinear diffusion is not computationally tractable. In this thesis, we
investigate a novel method of simplifying the problem. The stochastic differential
equation that describes the diffusion process is replaced with a simpler ordinary differential
equation, which has a random driving noise that approximates Brownian motion.
We show how one can exploit this approximation to improve on standard methods for
inferring properties of nonlinear diffusion processes
Bayesian learning of continuous time dynamical systems with applications in functional magnetic resonance imaging
Temporal phenomena in a range of disciplines are more naturally modelled in
continuous-time than coerced into a discrete-time formulation. Differential systems
form the mainstay of such modelling, in fields from physics to economics,
geoscience to neuroscience. While powerful, these are fundamentally limited by
their determinism. For the purposes of probabilistic inference, their extension
to stochastic differential equations permits a continuous injection of noise and
uncertainty into the system, the model, and its observation.
This thesis considers Bayesian filtering for state and parameter estimation in general
non-linear, non-Gaussian systems using these stochastic differential models.
It identifies a number of challenges in this setting over and above those of discrete
time, most notably the absence of a closed form transition density. These are addressed
via a synergy of diverse work in numerical integration, particle filtering
and high performance distributed computing, engineering novel solutions for this
class of model.
In an area where the default solution is linear discretisation, the first major
contribution is the introduction of higher-order numerical schemes, particularly
stochastic Runge-Kutta, for more efficient simulation of the system dynamics.
Improved runtime performance is demonstrated on a number of problems, and
compatibility of these integrators with conventional particle filtering and smoothing
schemes discussed.
Finding compatibility for the smoothing problem most lacking, the major theoretical
contribution of the work is the introduction of two novel particle methods, the
kernel forward-backward and kernel two-filter smoothers. By harnessing kernel
density approximations in an importance sampling framework, these attain cancellation
of the intractable transition density, ensuring applicability in continuous
time. The use of kernel estimators is particularly amenable to parallelisation, and
provides broader support for smooth densities than a sample-based representation
alone, helping alleviate the well known issue of degeneracy in particle smoothers.
Implementation of the methods for large-scale problems on high performance
computing architectures is provided. Achieving improved temporal and spatial
complexity, highly favourable runtime comparisons against conventional techniques are presented.
Finally, attention turns to real world problems in the domain of Functional
Magnetic Resonance Imaging (fMRI), first constructing a biologically motivated
stochastic differential model of the neural and hemodynamic activity underlying
the observed signal in fMRI. This model and the methodological advances of
the work culminate in application to the deconvolution and effective connectivity
problems in this domain
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