Miscellaneous Methods
This page covers specialized methods for particular types of problems or applications.
Composite Algorithms
StochasticCompositeAlgorithm - Multi-Method Solving
StochasticDiffEqCore.StochasticCompositeAlgorithm — Type
StochasticCompositeAlgorithm(algs, choice_function)Algorithm that switches between the members of algs from step to step.
choice_function(integrator) returns the index into algs of the member to use for the next step; the matching cache is held in a StochasticCompositeCache and selected through its current field.
This is the mechanism behind the automatic stiffness-switching solvers — see AutoAlgSwitch, which pairs a nonstiff and a stiff algorithm with an AutoSwitch choice function.
RODE Methods (Random ODEs)
RandomEM - Random Euler Method
RandomHeun - Random Heun Method
RandomTamedEM - Tamed Random Euler
Langevin Dynamics
BAOAB - Langevin Integrator
Predictor-Corrector Methods
PCEuler - Predictor-Corrector Euler
Integro-Integral-Form (IIF) Methods
IIF1M, IIF2M, IIF1Mil - IIF Methods
Simplified Methods
SimplifiedEM - Simplified Euler-Maruyama
When to Use Miscellaneous Methods
StochasticCompositeAlgorithm:
- When problem characteristics change during integration
- Combining methods for different regimes
- Automatic method switching based on conditions
RODE Methods:
- Random ordinary differential equations
- Problems with random parameters but no Brownian motion
- Uncertainty quantification applications
BAOAB:
- Molecular dynamics simulations
- Langevin equations with specific structure
- When preserving equilibrium distributions is important
IIF Methods:
- Semi-linear problems with stiff linear parts
- Problems amenable to integrating factor techniques
- When exponential integrators are appropriate
PCEuler:
- Problems requiring specific drift-diffusion coupling
- When analytical ggprime function is available
- Specialized predictor-corrector applications
These methods serve specific niches in stochastic computation and may be optimal for particular problem structures.