36 citations · 40 across the 3 of their papers we have counts for
4 papers · 1 filter
The blending region hybrid framework for the simulation of stochastic reaction-diffusion processes
Christian A. Yates, Adam George, Armand Jordana +3
The simulation of stochastic reaction-diffusion systems using fine-grained representations can become computationally prohibitive when particle numbers become large. If particle nu…
Probabilistic Gradients for Fast Calibration of Differential Equation Models
Jon Cockayne, Andrew B. Duncan
Calibration of large-scale differential equation models to observational or experimental data is a widespread challenge throughout applied sciences and engineering. A crucial bottl…
A Kernel Two-Sample Test for Functional Data
George Wynne, Andrew B. Duncan
We propose a nonparametric two-sample test procedure based on Maximum Mean Discrepancy (MMD) for testing the hypothesis that two samples of functions have the same underlying distr…
Manifold Learning for Accelerating Coarse-Grained Optimization
Dmitry Pozharskiy, Noah J. Wichrowski, Andrew B. Duncan +2
Algorithms proposed for solving high-dimensional optimization problems with no derivative information frequently encounter the "curse of dimensionality," becoming ineffective as th…