SciML/DiffEqFlux.jl

Turn an example from robotics and control into a tutorial?

Open

#477 opened on Jan 13, 2021

View on GitHub
 (4 comments) (0 reactions) (0 assignees)Julia (160 forks)batch import
good first issue

Repository metrics

Stars
 (919 stars)
PR merge metrics
 (Avg merge 1d 14h) (11 merged PRs in 30d)

Description

https://arxiv.org/abs/2012.06684

@samuela is there code to share that could become a tutorial? I think it would be good to make one.

Contributor guide

Research direction
Review the paper arXiv:2012.06684 to understand the robotics and control example, then create a step by step tutorial that demonstrates how to implement the example using DiffEqFlux.jl, explaining key concepts such as neural ODEs, adjoint methods, and loss functions.
Tech stack
None
Domain
documentationmachine learning
Issue type
Documentation
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
3-5 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Mostly clear
Prerequisites
JuliaBasic understanding of neural ODEs
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
50