SciML/SciMLBook resource
Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J) observed · 2026-08-28
Health v2 · maintenance only
67/100
- Activity 99
- Release rhythm 8
- Longevity 100
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2561
- days_rel: n/a
- days_push: 7
- n_releases_24m: 0
Adoption not part of the score
2036 stars · 375 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
An open online book compiling lecture notes from MIT course 18.337J/6.338J on parallel computing and scientific machine learning (SciML). It is a continuously updated Franklin.jl website covering differential equations, neural ODEs/SDEs, automatic differentiation, GPUs, and high-performance scientific computing.
Use cases
- learn scientific machine learning from scratch
- study neural ODEs and neural SDEs
- learn parallel computing and GPU programming for scientific computing
- understand how to solve stiff differential equations numerically
- find course materials on physics-informed machine learning
- learn automatic differentiation for differential equation solvers
- study performance engineering and profiling for scientific simulations
When to choose
- you want a free, in-depth textbook-style introduction to SciML and parallel computing
- you are teaching or self-studying differential equation solving combined with machine learning
- you want continuously updated lecture notes from an active research group
When to avoid
- you need runnable software or a library rather than reading material
- you want a language-agnostic or Python-only tutorial (examples are Julia-centric)
- you need a formal certification or instructor-led course rather than self-paced notes
Facets
learning-resource · maturity active
machine-learning gpu-computing simulation documentation math tutorials machine-learning gpu-computing jvm-scripting lecture-notes differential-equations neural-ode parallel-computing julia scientific-computing high-performance-computing mit-course scientific-machine-learning algorithms web-server
2 sources
- readme: https://github.com/SciML/SciMLBook · fetched 2026-08-28 · e7b048ffe289
- homepage: https://book.sciml.ai/ · fetched 2026-08-29 · 16a8cec92d79
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| SciML/SciMLBook | main | 67 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem