Walker: Random walker for stochastic differential equations
Walker is a fully asynchronous distributed-memory-parallel numerical integrator for stochastic differential equations (SDE). It can be used to analyze and design the evolution of fluctuating variables and their statistics in state space and time. Walker can compute time evolutions of arbitrary numbers of variables governed by an arbitrary number of coupled SDE systems of different types. It allows the estimation of arbitrary coupled statistics and probability density functions.
Walker is useful for designing statistical moment approximations. If the approximate shape of the probability distribution for a fluctuating variable is known, its governing SDE can, in some cases, be derived. Numerically integrating this governing equation, Walker allows extracting arbitrary statistics which can be used to test and verify hypotheses on functional forms of statistics as a function of other statistics. This enables, e.g., developing statistical moment models for turbulent flows where higher order statistics must be expressed in terms of lower order statistics based on some approximation.
Walker is also useful for designing approximations for probability density functions of fluctuating variables. If reasonable assumptions on probability distributions of some key variables of a process can be made, Walker can be used to integrate their governing equations and design parameterizations of those governing equations based on arbitrary statistics extracted from the computed distributions.
Walker uses the Charm++ runtime system to run concurrently, either on a single machine or a networked set of computers. The software design is fully asynchronous, yielding 100% CPU utilization at all times, and it exhibits perfect, i.e., 100% efficiency, weak scaling with \(3\!\times\!10^{12}\) particles up to 24K CPU cores, the most tested, see d5304e78.
Number-fraction beta - A system of SDEs, with linear drift and quadratic diagonal diffusion, whose invariant is the joint beta distribution. The number-fraction beta SDE is based on the beta SDE, additionally computing two more stochastic variables that are functions of the beta variables integrated, assuming that the beta variables are number, or mole-, fractions. For more details on the beta SDE, see Bakosi, Ristorcelli, Exploring the beta distribution in variable-density turbulent mixing, J. Turbul, 2010.
Mass-fraction beta - A system of SDEs, with linear drift and quadratic diagonal diffusion, whose invariant is the joint beta distribution. The mass-fraction beta SDE is based on the beta SDE, additionally computing two more stochastic variables that are functions of the beta variables integrated, assuming that the beta variables are mass-fractions. For more details on the beta SDE, see Bakosi, Ristorcelli, Exploring the beta distribution in variable-density turbulent mixing, J. Turbul, 2010.
Mix number-fraction beta - A system of SDEs, with linear drift and quadratic diagonal diffusion, whose invariant is the joint beta distribution. The mix number-fraction beta SDE is based on the beta SDE, with the following differences: (1) the SDE coefficients are specified consistent with the binary turbulent mixing process, and (2) additionally computing two more stochastic variables that are functions of the beta variables integrated, assuming that the beta variables are number, or mole-, fractions. For more details on the beta SDE, see Bakosi, Ristorcelli, Exploring the beta distribution in variable-density turbulent mixing, J. Turbul, 2010.
Mix mass-fraction beta - A system of SDEs, with linear drift and quadratic diagonal diffusion, whose invariant is the joint beta distribution. The mix mass-fraction beta SDE is based on the beta SDE, with the following differences: (1) the SDE coefficients are specified consistent with the binary turbulent mixing process, and (2) additionally computing two more stochastic variables that are functions of the beta variables integrated, assuming that the beta variables are mass-fractions. For more details on the beta SDE, see Bakosi, Ristorcelli, Exploring the beta distribution in variable-density turbulent mixing, J. Turbul, 2010.