Abstract: Simultaneous perturbation stochastic approximation method was shown to be superior over finite difference (Kiefer-Wolfowitz) method in case of unknown but bounded additive measurement noise.

Abstract: Estimating stochastic gradients is pivotal in fields like service systems in operations research. The classical method for this estimation is the finite-difference approximation, which ...

The FD= and FDHESSIAN= options specify the use of finite difference approximations of the derivatives. The FD= option specifies that all derivatives are approximated using function evaluations, and ...

This project explores edge detection in images using central finite difference approximations of image derivatives. It includes Python implementations using OpenCV and NumPy, demonstrating the effect ...

ABSTRACT: The goal of computational science is to develop models that predict phenomena observed in nature. However, these models are often based on parameters that are uncertain. In recent decades, ...

ABSTRACT: In this paper, for the initial and boundary value problem of beams with structural damping, by introducing intermediate variables, the original fourth-order problem is transformed into ...

Developed a CUDA version of the FDTD method and achieved a speedup 40x. Implemented on a NVIDIA Quadro FX 3800 GPU, which has 192 SPs, 1GB global memory, and a memory bandwidth of 51.2 GB/s.