https://github.com/sciml/surrogates.jl
Surrogate modeling and optimization for scientific machine learning (SciML)
Science Score: 59.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 3 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
✓Committers with academic emails
3 of 57 committers (5.3%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (14.4%) to scientific vocabulary
Keywords
automatic-differentiation
differential-equations
high-performance-computing
julia
optimization
scientific-machine-learning
sciml
surrogate
surrogate-based-optimization
surrogate-models
surrogates
Keywords from Contributors
ode
neural-sde
symbolic-computation
differentialequations
julialang
ordinary-differential-equations
pde
sde
numerical
pinn
Last synced: 6 months ago
·
JSON representation
Repository
Surrogate modeling and optimization for scientific machine learning (SciML)
Basic Info
- Host: GitHub
- Owner: SciML
- License: other
- Language: Julia
- Default Branch: master
- Homepage: https://docs.sciml.ai/Surrogates/stable/
- Size: 327 MB
Statistics
- Stars: 350
- Watchers: 11
- Forks: 76
- Open Issues: 30
- Releases: 48
Topics
automatic-differentiation
differential-equations
high-performance-computing
julia
optimization
scientific-machine-learning
sciml
surrogate
surrogate-based-optimization
surrogate-models
surrogates
Created almost 7 years ago
· Last pushed 6 months ago
Metadata Files
Readme
Contributing
License
README.md
Surrogates.jl
A surrogate model is an approximation method that mimics the behavior of a computationally
expensive simulation. In more mathematical terms: suppose we are attempting to optimize a function
f(p), but each calculation of f is very expensive. It may be the case we need to solve a PDE for each point or use advanced numerical linear algebra machinery, which is usually costly. The idea is then to develop a surrogate model g which approximates f by training on previous data collected from evaluations of f.
The construction of a surrogate model can be seen as a three-step process:
- Sample selection
- Construction of the surrogate model
- Surrogate optimization
Sampling can be done through QuasiMonteCarlo.jl, all the functions available there can be used in Surrogates.jl.
ALL the currently available surrogate models:
- Kriging
- Kriging using Stheno
- Radial Basis
- Wendland
- Linear
- Second Order Polynomial
- Support Vector Machines (Wait for LIBSVM resolution)
- Neural Networks
- Random Forests
- Lobachevsky
- Inverse-distance
- Polynomial expansions
- Variable fidelity
- Mixture of experts (Waiting GaussianMixtures package to work on v1.5)
- Earth
- Gradient Enhanced Kriging
ALL the currently available optimization methods:
- SRBF
- LCBS
- DYCORS
- EI
- SOP
- Multi-optimization: SMB and RTEA
Installing Surrogates package
julia
using Pkg
Pkg.add("Surrogates")
Owner
- Name: SciML Open Source Scientific Machine Learning
- Login: SciML
- Kind: organization
- Email: contact@chrisrackauckas.com
- Website: https://sciml.ai
- Twitter: SciML_Org
- Repositories: 170
- Profile: https://github.com/SciML
Open source software for scientific machine learning
GitHub Events
Total
- Create event: 14
- Release event: 1
- Issues event: 14
- Watch event: 18
- Delete event: 11
- Issue comment event: 48
- Push event: 48
- Pull request review comment event: 8
- Pull request review event: 10
- Pull request event: 36
- Fork event: 8
Last Year
- Create event: 14
- Release event: 1
- Issues event: 14
- Watch event: 18
- Delete event: 11
- Issue comment event: 48
- Push event: 48
- Pull request review comment event: 8
- Pull request review event: 10
- Pull request event: 36
- Fork event: 8
Committers
Last synced: 8 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| ludoro | l****i@g****m | 287 |
| Christopher Rackauckas | a****s@c****m | 136 |
| Sathvik Bhagavan | s****n@g****m | 69 |
| Vikram | v****n@g****m | 65 |
| github-actions[bot] | 4****] | 29 |
| Thomas Marks | m****a@u****u | 28 |
| marcoq | m****i@g****m | 26 |
| Arno Strouwen | a****n@t****e | 25 |
| Ranjan Anantharaman | r****n@g****m | 25 |
| Rohit Singh Rathaur | 4****N | 22 |
| TeAmp0is0N | r****5@g****m | 21 |
| Andrea Cognolato | a****o@h****t | 18 |
| dependabot[bot] | 4****] | 18 |
| Dreycen Foiles | f****n@g****m | 15 |
| Pawel Latawiec | p****c@h****m | 14 |
| CompatHelper Julia | c****y@j****g | 13 |
| Ashutosh Bharambe | a****3@g****m | 12 |
| michiboo | c****n@g****m | 7 |
| Anant Thazhemadam | a****m@g****m | 7 |
| Fergus Baker | f****r@g****m | 6 |
| Sharan Yalburgi | s****i@g****m | 6 |
| Juergen Fuhrmann | j****n@w****e | 5 |
| ST John | s****- | 4 |
| Johanni Brea | j****a | 3 |
| Chris de Graaf | me@c****v | 2 |
| Dishebh Bhayana | 4****h | 2 |
| Jeffrey Sarnoff | J****f | 2 |
| Kanav Gupta | 3****9 | 2 |
| MartinuzziFrancesco | m****o@g****m | 2 |
| Morten Piibeleht | m****t@g****m | 2 |
| and 27 more... | ||
Committer Domains (Top 20 + Academic)
ranocha.de: 1
qq.com: 1
mailbox.org: 1
gmx.net: 1
cmla.ens-cachan.fr: 1
me.com: 1
fudan.edu.cn: 1
llaway.com: 1
bu.edu: 1
cdg.dev: 1
wias-berlin.de: 1
julialang.org: 1
hotmail.it: 1
telenet.be: 1
umich.edu: 1
chrisrackauckas.com: 1
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 58
- Total pull requests: 145
- Average time to close issues: 11 months
- Average time to close pull requests: about 1 month
- Total issue authors: 28
- Total pull request authors: 27
- Average comments per issue: 6.26
- Average comments per pull request: 1.5
- Merged pull requests: 111
- Bot issues: 0
- Bot pull requests: 44
Past Year
- Issues: 5
- Pull requests: 33
- Average time to close issues: about 2 months
- Average time to close pull requests: 4 days
- Issue authors: 4
- Pull request authors: 9
- Average comments per issue: 1.4
- Average comments per pull request: 0.52
- Merged pull requests: 28
- Bot issues: 0
- Bot pull requests: 14
Top Authors
Issue Authors
- vikram-s-narayan (15)
- ChrisRackauckas (5)
- archermarx (4)
- ArnoStrouwen (4)
- jacktang (3)
- chenr86 (2)
- sharanry (2)
- JinraeKim (2)
- mjowen (2)
- sleepingPhD (1)
- sdwfrost (1)
- 00sapo (1)
- JuliaTagBot (1)
- gaelforget (1)
- MartinuzziFrancesco (1)
Pull Request Authors
- dependabot[bot] (34)
- sathvikbhagavan (28)
- github-actions[bot] (27)
- ArnoStrouwen (20)
- vikram-s-narayan (14)
- Spinachboul (9)
- archermarx (7)
- thazhemadam (7)
- ChrisRackauckas (5)
- ranjanan (3)
- mfschubert (2)
- MartinuzziFrancesco (2)
- spalato (2)
- 00krishna (2)
- mortenpi (1)
Top Labels
Issue Labels
bug (5)
Pull Request Labels
dependencies (34)
Packages
- Total packages: 7
-
Total downloads:
- julia 115 total
-
Total dependent packages: 11
(may contain duplicates) -
Total dependent repositories: 0
(may contain duplicates) - Total versions: 61
juliahub.com: Surrogates
Surrogate modeling and optimization for scientific machine learning (SciML)
- Homepage: https://docs.sciml.ai/Surrogates/stable/
- Documentation: https://docs.juliahub.com/General/Surrogates/stable/
- License: MIT
-
Latest release: 7.0.1
published 7 months ago
Rankings
Forks count: 1.9%
Stargazers count: 2.6%
Average: 5.4%
Dependent packages count: 7.0%
Dependent repos count: 9.9%
Last synced:
6 months ago
juliahub.com: SurrogatesPolyChaos
Surrogate modeling and optimization for scientific machine learning (SciML)
- Homepage: https://docs.sciml.ai/Surrogates/stable/
- Documentation: https://docs.juliahub.com/General/SurrogatesPolyChaos/stable/
- License: MIT
-
Latest release: 0.1.1
published 11 months ago
Rankings
Forks count: 1.9%
Stargazers count: 2.6%
Average: 9.3%
Dependent repos count: 9.9%
Dependent packages count: 23.0%
Last synced:
6 months ago
juliahub.com: SurrogatesFlux
Surrogate modeling and optimization for scientific machine learning (SciML)
- Homepage: https://docs.sciml.ai/Surrogates/stable/
- Documentation: https://docs.juliahub.com/General/SurrogatesFlux/stable/
- License: MIT
-
Latest release: 0.1.1
published about 1 year ago
Rankings
Forks count: 1.9%
Stargazers count: 2.6%
Average: 9.3%
Dependent repos count: 9.9%
Dependent packages count: 23.0%
Last synced:
6 months ago
juliahub.com: SurrogatesRandomForest
Surrogate modeling and optimization for scientific machine learning (SciML)
- Homepage: https://docs.sciml.ai/Surrogates/stable/
- Documentation: https://docs.juliahub.com/General/SurrogatesRandomForest/stable/
- License: MIT
-
Latest release: 0.1.2
published 11 months ago
Rankings
Forks count: 1.9%
Stargazers count: 2.6%
Average: 9.3%
Dependent repos count: 9.9%
Dependent packages count: 23.0%
Last synced:
6 months ago
juliahub.com: SurrogatesMOE
Surrogate modeling and optimization for scientific machine learning (SciML)
- Homepage: https://docs.sciml.ai/Surrogates/stable/
- Documentation: https://docs.juliahub.com/General/SurrogatesMOE/stable/
- License: MIT
-
Latest release: 0.1.5
published 11 months ago
Rankings
Forks count: 1.9%
Stargazers count: 2.6%
Dependent repos count: 9.9%
Average: 13.3%
Dependent packages count: 38.9%
Last synced:
6 months ago
juliahub.com: SurrogatesAbstractGPs
Surrogate modeling and optimization for scientific machine learning (SciML)
- Homepage: https://docs.sciml.ai/Surrogates/stable/
- Documentation: https://docs.juliahub.com/General/SurrogatesAbstractGPs/stable/
- License: MIT
-
Latest release: 0.1.1
published 11 months ago
Rankings
Forks count: 1.9%
Stargazers count: 2.6%
Dependent repos count: 9.9%
Average: 13.3%
Dependent packages count: 38.9%
Last synced:
6 months ago
juliahub.com: SurrogatesSVM
Surrogate modeling and optimization for scientific machine learning (SciML)
- Homepage: https://docs.sciml.ai/Surrogates/stable/
- Documentation: https://docs.juliahub.com/General/SurrogatesSVM/stable/
- License: MIT
-
Latest release: 0.1.1
published 11 months ago
Rankings
Forks count: 1.9%
Stargazers count: 2.6%
Dependent repos count: 9.9%
Average: 13.3%
Dependent packages count: 38.9%
Last synced:
6 months ago
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