MAGI: A Package for Inference of Dynamic Systems from Noisy and Sparse Data via Manifold-constrained Gaussian Processes Article Swipe
Samuel W. K. Wong
,
Shu Yang
,
S. C. Kou
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2203.06066
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2203.06066
This article presents the MAGI software package for the inference of dynamic systems. The focus of MAGI is on dynamics modeled by nonlinear ordinary differential equations with unknown parameters. While such models are widely used in science and engineering, the available experimental data for parameter estimation may be noisy and sparse. Furthermore, some system components may be entirely unobserved. MAGI solves this inference problem with the help of manifold-constrained Gaussian processes within a Bayesian statistical framework, whereas unobserved components have posed a significant challenge for existing software. We use several realistic examples to illustrate the functionality of MAGI. The user may choose to use the package in any of the R, MATLAB, and Python environments.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2203.06066
- https://arxiv.org/pdf/2203.06066
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221154119
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- OpenAlex ID
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https://openalex.org/W4221154119Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2203.06066Digital Object Identifier
- Title
-
MAGI: A Package for Inference of Dynamic Systems from Noisy and Sparse Data via Manifold-constrained Gaussian ProcessesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-03-11Full publication date if available
- Authors
-
Samuel W. K. Wong, Shu Yang, S. C. KouList of authors in order
- Landing page
-
https://arxiv.org/abs/2203.06066Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2203.06066Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2203.06066Direct OA link when available
- Concepts
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Python (programming language), Computer science, Inference, MATLAB, Software package, Software, Bayesian inference, Gaussian, Nonlinear system, Statistical inference, Bayesian probability, Algorithm, Data mining, Artificial intelligence, Mathematics, Statistics, Programming language, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.nonlinear | 22 |
| abstract_inverted_index.parameter | 44 |
| abstract_inverted_index.processes | 70 |
| abstract_inverted_index.realistic | 90 |
| abstract_inverted_index.software. | 86 |
| abstract_inverted_index.components | 54, 78 |
| abstract_inverted_index.estimation | 45 |
| abstract_inverted_index.framework, | 75 |
| abstract_inverted_index.illustrate | 93 |
| abstract_inverted_index.unobserved | 77 |
| abstract_inverted_index.parameters. | 28 |
| abstract_inverted_index.significant | 82 |
| abstract_inverted_index.statistical | 74 |
| abstract_inverted_index.unobserved. | 58 |
| abstract_inverted_index.Furthermore, | 51 |
| abstract_inverted_index.differential | 24 |
| abstract_inverted_index.engineering, | 38 |
| abstract_inverted_index.experimental | 41 |
| abstract_inverted_index.environments. | 114 |
| abstract_inverted_index.functionality | 95 |
| abstract_inverted_index.manifold-constrained | 68 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |