Monte Carlo simulation is widely used to numerically solve stochastic differential equations. Although the method is flexible and easy to implement, it may be slow to converge. Moreover, an inaccurate solution will result when using large time steps. The Seven League scheme, a deep learning-based numerical method, has been proposed to address these issues. This paper generalizes the scheme regarding parallel computing, particularly on Graphics Processing Units (GPUs), improving the computational speed
Motivation: The importance of stochasticity in biological systems is becoming increasingly recognize...
Massively parallel computer architectures create new opportunities for the performance of long-time ...
The curse-of-dimensionality (CoD) taxes computational resources heavily with exponentially increasin...
Monte Carlo simulation is widely used to numerically solve stochastic differential equations. Althou...
We propose an accurate data-driven numerical scheme to solve stochastic differential equations (SDEs...
We propose an accurate data-driven numerical scheme to solve stochastic differential equations (SDEs...
In this paper, we design a novel algorithm based on Least-Squares Monte Carlo (LSMC) in order to app...
The main purpose of this work was to develop a more time efficient solution to the Lotka- Volterra m...
The Gillespie Stochastic Simulation Algorithm (GSSA) and its variants are cornerstone techniques to ...
We present a case study on the utility of graphics cards to perform massively parallel simulation of...
<div><p>The Gillespie Stochastic Simulation Algorithm (GSSA) and its variants are cornerstone techni...
We explore the performance of several algorithms for the solution of stochastic partial differential...
In this article we design a novel quasi-regression Monte Carlo algorithm in order to approximate the...
The objective of this Final Year Project is to study deep learning-based numerical methods, with a f...
We present a case-study on the utility of graphics cards to perform massively parallel simulation of...
Motivation: The importance of stochasticity in biological systems is becoming increasingly recognize...
Massively parallel computer architectures create new opportunities for the performance of long-time ...
The curse-of-dimensionality (CoD) taxes computational resources heavily with exponentially increasin...
Monte Carlo simulation is widely used to numerically solve stochastic differential equations. Althou...
We propose an accurate data-driven numerical scheme to solve stochastic differential equations (SDEs...
We propose an accurate data-driven numerical scheme to solve stochastic differential equations (SDEs...
In this paper, we design a novel algorithm based on Least-Squares Monte Carlo (LSMC) in order to app...
The main purpose of this work was to develop a more time efficient solution to the Lotka- Volterra m...
The Gillespie Stochastic Simulation Algorithm (GSSA) and its variants are cornerstone techniques to ...
We present a case study on the utility of graphics cards to perform massively parallel simulation of...
<div><p>The Gillespie Stochastic Simulation Algorithm (GSSA) and its variants are cornerstone techni...
We explore the performance of several algorithms for the solution of stochastic partial differential...
In this article we design a novel quasi-regression Monte Carlo algorithm in order to approximate the...
The objective of this Final Year Project is to study deep learning-based numerical methods, with a f...
We present a case-study on the utility of graphics cards to perform massively parallel simulation of...
Motivation: The importance of stochasticity in biological systems is becoming increasingly recognize...
Massively parallel computer architectures create new opportunities for the performance of long-time ...
The curse-of-dimensionality (CoD) taxes computational resources heavily with exponentially increasin...