During the seven years that elapsed between the first and second editions of the present book, considerable progress was achieved in the area of financial modelling and pricing of …
P Xu, J Chen, D Zou, Q Gu - Advances in Neural …, 2018 - proceedings.neurips.cc
We present a unified framework to analyze the global convergence of Langevin dynamics based algorithms for nonconvex finite-sum optimization with $ n $ component functions. At …
Y Saito, T Mitsui - SIAM Journal on Numerical Analysis, 1996 - SIAM
Stochastic differential equations (SDEs) represent physical phenomena dominated by stochastic processes. As for deterministic ordinary differential equations (ODEs), various …
TK Flesch, JD Wilson, E Yee - Journal of Applied Meteorology …, 1995 - journals.ametsoc.org
Abstract “Backward” Lagrangian stochastic models calculate an ensemble of fluid element (particle) trajectories that are distinguished by each passing through an observation point …
This paper aims to give an overview and summary of numerical methods for the solution of stochastic differential equations. It covers discrete time strong and weak approximation …
GN Milstein, E Platen, H Schurz - SIAM Journal on Numerical Analysis, 1998 - SIAM
This paper introduces some implicitness in stochastic terms of numerical methods for solving stiff stochastic differential equations and especially aclass of fully implicit methods, the …
A framework for creating and updating digital twins for dynamical systems from a library of physics-based functions is proposed. The sparse Bayesian machine learning is used to …
Extracting governing physics from data is a key challenge in many areas of science and technology. The existing techniques for equation discovery are mostly applicable to …
A Miao, X Wang, T Zhang, W Wang… - Advances in Difference …, 2017 - Springer
In this paper, a stochastic SIS epidemic model with nonlinear incidence rate and double epidemic hypothesis is proposed and analysed. We explain the effects of stochastic …