Transforms for Brownian dynamics

Initial release.

Brownian dynamics integrators with coordinate transforms to constant diffusion, implemented in Julia.
This codebase was developed for the paper "Numerical Methods with Coordinate Transforms for Efficient Brownian Dynamics Simulations".



Numerical integrators of Stochastic Differential Equations (SDEs) work well for constant (additive) noise but often lose performance, or fail to converge, for variable (multiplicative) noise. One solution, if and when it is possible, is to apply reversible transforms in space or time to convert a variable noise process into a constant noise process, which can be more efficiently sampled with traditional SDE integrators. Then, trajectories can be reweighted and the statistical and dynamical properties of the original process can be reconstructed.
Brownian dynamics is one of the most important classes of SDE process, with applications across the physical, biological, and data-driven sciences. This codebase provides a Julia toolbox for running high-efficiency simulations of Brownian dynamics processes with coordinate transforms.

Dom Phillips. (2023). dominicp6/Transforms-For-Brownian-Dynamics: Initial Release (v0.1.0). Zenodo. https://doi.org/10.5281/zenodo.8434931