1,554 research outputs found
Numerical Solutions of Stochastic Differential Equations Driven by Poisson Random Measure with Non-Lipschitz Coefficients
The numerical methods in the current known literature require the stochastic differential equations SDEs driven by Poisson random measure satisfying the global Lipschitz condition and the linear growth condition. In this paper, Euler's method is introduced for SDEs driven by Poisson random measure with non-Lipschitz coefficients which cover more classes of such equations than before. The main aim is to investigate the convergence of the Euler method in probability to such equations with non-Lipschitz coefficients. Numerical example is given to demonstrate our results
Model Reduction and Neural Networks for Parametric PDEs
We develop a general framework for data-driven approximation of input-output maps between infinite-dimensional spaces. The proposed approach is motivated by the recent successes of neural networks and deep learning, in combination with ideas from model reduction. This combination results in a neural network approximation which, in principle, is defined on infinite-dimensional spaces and, in practice, is robust to the dimension of finite-dimensional approximations of these spaces required for computation. For a class of input-output maps, and suitably chosen probability measures on the inputs, we prove convergence of the proposed approximation methodology. Numerically we demonstrate the effectiveness of the method on a class of parametric elliptic PDE problems, showing convergence and robustness of the approximation scheme with respect to the size of the discretization, and compare our method with existing algorithms from the literature
On the stability of stochastic jump kinetics
Motivated by the lack of a suitable constructive framework for analyzing
popular stochastic models of Systems Biology, we devise conditions for
existence and uniqueness of solutions to certain jump stochastic differential
equations (SDEs). Working from simple examples we find reasonable and explicit
assumptions on the driving coefficients for the SDE representation to make
sense. By `reasonable' we mean that stronger assumptions generally do not hold
for systems of practical interest. In particular, we argue against the
traditional use of global Lipschitz conditions and certain common growth
restrictions. By `explicit', finally, we like to highlight the fact that the
various constants occurring among our assumptions all can be determined once
the model is fixed.
We show how basic long time estimates and some limit results for
perturbations can be derived in this setting such that these can be contrasted
with the corresponding estimates from deterministic dynamics. The main
complication is that the natural path-wise representation is generated by a
counting measure with an intensity that depends nonlinearly on the state
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