A thin wrapper over Bridge.jl for the SciML scientific machine learning common interface, enabling new methods for neural stochastic differential equations (neural SDEs)
Author SciML
4 Stars
Updated Last
5 Months Ago
Started In
August 2017


Build Status Coverage Status

This package contains bindings for Bridge.jl to allow it to be used with the JuliaDiffEq common interface. For more information on using the solvers from this package, see the DifferentialEquations.jl documentation.

Common API Usage

This library adds the common interface to Bridge.jl's solvers. See the DifferentialEquations.jl documentation for details on the interface. Following the Black-Scholes example from the SDE tutorial, we can solve this using BridgeEuler via the following:

f(u,p,t) = α*u
g(u,p,t) = β*u
dt = 1//2^(4)
tspan = (0.0,1.0)
prob = SDEProblem(f,g,u0,(0.0,1.0))
sol = solve(prob,BridgeEuler(),dt=dt)
using Plots; plot(sol,vars=(1,2,3))

The options available in solve are documented at the common solver options page. The available methods are documented at the ODE solvers page and at the SDE solvers page.

Used By Packages

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