73 research outputs found
Algebraic structure of stochastic expansions and efficient simulation
We investigate the algebraic structure underlying the stochastic Taylor
solution expansion for stochastic differential systems.Our motivation is to
construct efficient integrators. These are approximations that generate strong
numerical integration schemes that are more accurate than the corresponding
stochastic Taylor approximation, independent of the governing vector fields and
to all orders. The sinhlog integrator introduced by Malham & Wiese (2009) is
one example. Herein we: show that the natural context to study stochastic
integrators and their properties is the convolution shuffle algebra of
endomorphisms; establish a new whole class of efficient integrators; and then
prove that, within this class, the sinhlog integrator generates the optimal
efficient stochastic integrator at all orders.Comment: 19 page
An integral method for solving nonlinear eigenvalue problems
We propose a numerical method for computing all eigenvalues (and the
corresponding eigenvectors) of a nonlinear holomorphic eigenvalue problem that
lie within a given contour in the complex plane. The method uses complex
integrals of the resolvent operator, applied to at least column vectors,
where is the number of eigenvalues inside the contour. The theorem of
Keldysh is employed to show that the original nonlinear eigenvalue problem
reduces to a linear eigenvalue problem of dimension .
No initial approximations of eigenvalues and eigenvectors are needed. The
method is particularly suitable for moderately large eigenvalue problems where
is much smaller than the matrix dimension. We also give an extension of the
method to the case where is larger than the matrix dimension. The
quadrature errors caused by the trapezoid sum are discussed for the case of
analytic closed contours. Using well known techniques it is shown that the
error decays exponentially with an exponent given by the product of the number
of quadrature points and the minimal distance of the eigenvalues to the
contour
Approximate probabilistic verification of hybrid systems
Hybrid systems whose mode dynamics are governed by non-linear ordinary
differential equations (ODEs) are often a natural model for biological
processes. However such models are difficult to analyze. To address this, we
develop a probabilistic analysis method by approximating the mode transitions
as stochastic events. We assume that the probability of making a mode
transition is proportional to the measure of the set of pairs of time points
and value states at which the mode transition is enabled. To ensure a sound
mathematical basis, we impose a natural continuity property on the non-linear
ODEs. We also assume that the states of the system are observed at discrete
time points but that the mode transitions may take place at any time between
two successive discrete time points. This leads to a discrete time Markov chain
as a probabilistic approximation of the hybrid system. We then show that for
BLTL (bounded linear time temporal logic) specifications the hybrid system
meets a specification iff its Markov chain approximation meets the same
specification with probability . Based on this, we formulate a sequential
hypothesis testing procedure for verifying -approximately- that the Markov
chain meets a BLTL specification with high probability. Our case studies on
cardiac cell dynamics and the circadian rhythm indicate that our scheme can be
applied in a number of realistic settings
Mean-square stability analysis of approximations of stochastic differential equations in infinite dimensions
Almost sure stability of the Euler-Maruyama method with random variable stepsize for stochastic differential equations
In this paper, the Euler–Maruyama (EM) method with random variable stepsize is studied to reproduce the almost sure stability of the true solutions of stochastic differential equations. Since the choice of the time step is based on the current state of the solution, the time variable is proved to be a stopping time. Then the semimartingale convergence theory is employed to obtain the almost sure stability of the random variable stepsize EM solution. To our best knowledge, this is the first paper to apply the random variable stepsize (with clear proof of the stopping time) to the analysis of the almost sure stability of the EM method
Rapid covariance-based sampling of linear SPDE approximations in the multilevel Monte Carlo method
The efficient simulation of the mean value of a non-linear functional of the
solution to a linear stochastic partial differential equation (SPDE) with
additive Gaussian noise is considered. A Galerkin finite element method is
employed along with an implicit Euler scheme to arrive at a fully discrete
approximation of the mild solution to the equation. A scheme is presented to
compute the covariance of this approximation, which allows for rapid sampling
in a Monte Carlo method. This is then extended to a multilevel Monte Carlo
method, for which a scheme to compute the cross-covariance between the
approximations at different levels is presented. In contrast to traditional
path-based methods it is not assumed that the Galerkin subspaces at these
levels are nested. The computational complexities of the presented schemes are
compared to traditional methods and simulations confirm that, under suitable
assumptions, the costs of the new schemes are significantly lower.Comment: 18 pages, 5 figures; numerical simulations revised, implementation
section added; To appear in Monte Carlo and Quasi-Monte Carlo Methods -
MCQMC, Rennes, France, July 201
Deep learning-assisted radiomics facilitates multimodal prognostication for personalized treatment strategies in low-grade glioma
Determining the optimal course of treatment for low grade glioma (LGG) patients is challenging and frequently reliant on subjective judgment and limited scientific evidence. Our objective was to develop a comprehensive deep learning assisted radiomics model for assessing not only overall survival in LGG, but also the likelihood of future malignancy and glioma growth velocity. Thus, we retrospectively included 349 LGG patients to develop a prediction model using clinical, anatomical, and preoperative MRI data. Before performing radiomics analysis, a U2-model for glioma segmentation was utilized to prevent bias, yielding a mean whole tumor Dice score of 0.837. Overall survival and time to malignancy were estimated using Cox proportional hazard models. In a postoperative model, we derived a C-index of 0.82 (CI 0.79-0.86) for the training cohort over 10Â years and 0.74 (Cl 0.64-0.84) for the test cohort. Preoperative models showed a C-index of 0.77 (Cl 0.73-0.82) for training and 0.67 (Cl 0.57-0.80) test sets. Our findings suggest that we can reliably predict the survival of a heterogeneous population of glioma patients in both preoperative and postoperative scenarios. Further, we demonstrate the utility of radiomics in predicting biological tumor activity, such as the time to malignancy and the LGG growth rate
Explicit methods for stiff stochastic differential equations
Multiscale differential equations arise in the modeling of many important problems in the science and engineering. Numerical solvers for such problems have been extensively studied in the deterministic case. Here, we discuss numerical methods for (mean-square stable) stiff stochastic differential equations. Standard explicit methods, as for example the Euler-Maruyama method, face severe stepsize restriction when applied to stiff problems. Fully implicit methods are usually not appropriate for stochastic problems and semi-implicit methods (implicit in the deterministic part) involve the solution of possibly large linear systems at each time-step. In this paper, we present a recent generalization of explicit stabilized methods, known as Chebyshev methods, to stochastic problems. These methods have much better (mean-square) stability properties than standard explicit methods. We discuss the construction of this new class of methods and illustrate their performance on various problems involving stochastic ordinary and partial differential equations
Classical robots perturbed by Lévy processes: analysis and Lévy disturbance rejection methods
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