https://github.com/avik-pal/boundaryvaluediffeq.jl
Boundary value problem (BVP) solvers for scientific machine learning (SciML)
Science Score: 18.0%
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Repository
Boundary value problem (BVP) solvers for scientific machine learning (SciML)
Basic Info
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Metadata Files
README.md
BoundaryValueDiffEq
BoundaryValueDiffEq.jl is a component package in the DifferentialEquations ecosystem. It holds the boundary value problem solvers and utilities. While completely independent and usable on its own, users interested in using this functionality should check out DifferentialEquations.jl.
API
BoundaryValueDiffEq.jl is part of the JuliaDiffEq common interface, but can be used independently of DifferentialEquations.jl. The only requirement is that the user passes a BoundaryValueDiffEq.jl algorithm to solve. For example, we can solve the BVP tutorial from the documentation using the MIRK4() algorithm:
julia
using BoundaryValueDiffEq
tspan = (0.0, pi / 2)
function simplependulum!(du, u, p, t)
θ = u[1]
dθ = u[2]
du[1] = dθ
du[2] = -9.81 * sin(θ)
end
function bc!(residual, u, p, t)
residual[1] = u[end ÷ 2][1] + pi / 2
residual[2] = u[end][1] - pi / 2
end
prob = BVProblem(simplependulum!, bc!, [pi / 2, pi / 2], tspan)
sol = solve(prob, MIRK4(), dt = 0.05)
Available Solvers
For the list of available solvers, please refer to the DifferentialEquations.jl BVP Solvers page. For options for the solve command, see the common solver options page.
Controlling Precompilation
Precompilation can be controlled via Preferences.jl
PrecompileMIRK-- Precompile the MIRK2 - MIRK6 algorithms (default:true).PrecompileShooting-- Precompile the single shooting algorithms (default:false). This is triggered whenOrdinaryDiffEqis loaded.PrecompileMultipleShooting-- Precompile the multiple shooting algorithms (default:false). This is triggered whenOrdinaryDiffEqis loaded.PrecompileMIRKNLLS-- Precompile the MIRK2 - MIRK6 algorithms for under-determined and over-determined BVPs (default:false).PrecompileShootingNLLS-- Precompile the single shooting algorithms for under-determined and over-determined BVPs (default:false). This is triggered whenOrdinaryDiffEqis loaded.PrecompileMultipleShootingNLLS-- Precompile the multiple shooting algorithms for under-determined and over-determined BVPs (default:false). This is triggered whenOrdinaryDiffEqis loaded.
To set these preferences before loading the package, do the following (replacing PrecompileShooting with the preference you want to set, or pass in multiple pairs to set them together):
julia
using Preferences, UUIDs
Preferences.set_preferences!(UUID("764a87c0-6b3e-53db-9096-fe964310641d"),
"PrecompileShooting" => false)
Running Benchmarks Locally
We include a small set of benchmarks in the benchmarks folder. These are not extensive and mainly used to track regressions during development. For more extensive benchmarks, see the SciMLBenchmarks repository.
To run benchmarks locally install AirspeedVelocity.jl and run the following command in the package directory:
bash
benchpkg BoundaryValueDiffEq --rev="master,<git sha for your commit>" --bench-on="master"
Owner
- Name: Avik Pal
- Login: avik-pal
- Kind: user
- Location: Cambridge, MA
- Company: Massachusetts Institute of Technology
- Website: https://avik-pal.github.io
- Twitter: avikpal1410
- Repositories: 46
- Profile: https://github.com/avik-pal
PhD Student @mit || Prev: BTech CSE IITK
Citation (CITATION.bib)
@article{DifferentialEquations.jl-2017,
author = {Rackauckas, Christopher and Nie, Qing},
doi = {10.5334/jors.151},
journal = {The Journal of Open Research Software},
keywords = {Applied Mathematics},
note = {Exported from https://app.dimensions.ai on 2019/05/05},
number = {1},
pages = {},
title = {DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia},
url = {https://app.dimensions.ai/details/publication/pub.1085583166 and http://openresearchsoftware.metajnl.com/articles/10.5334/jors.151/galley/245/download/},
volume = {5},
year = {2017}
}
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