Gradient-free training of neural ODEs for system identification and control using ensemble Kalman inversion Article Swipe
Ensemble Kalman inversion (EKI) is a sequential Monte Carlo method used to solve inverse problems within a Bayesian framework. Unlike backpropagation, EKI is a gradient-free optimization method that only necessitates the evaluation of artificial neural networks in forward passes. In this study, we examine the effectiveness of EKI in training neural ordinary differential equations (neural ODEs) for system identification and control tasks. To apply EKI to optimal control problems, we formulate inverse problems that incorporate a Tikhonov-type regularization term. Our numerical results demonstrate that EKI is an efficient method for training neural ODEs in system identification and optimal control problems, with runtime and quality of solutions that are competitive with commonly used gradient-based optimizers.
Related Topics
Concepts
Tikhonov regularization
Artificial neural network
Computer science
Backpropagation
Inversion (geology)
Ode
Inverse problem
Regularization (linguistics)
Optimal control
Ordinary differential equation
Gradient descent
Identification (biology)
Mathematical optimization
Artificial intelligence
Mathematics
Applied mathematics
Differential equation
Biology
Paleontology
Botany
Structural basin
Mathematical analysis
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2307.07882
- https://arxiv.org/pdf/2307.07882
- OA Status
- green
- Cited By
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4384644017
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4384644017Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2307.07882Digital Object Identifier
- Title
-
Gradient-free training of neural ODEs for system identification and control using ensemble Kalman inversionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-15Full publication date if available
- Authors
-
Lucas BöttcherList of authors in order
- Landing page
-
https://arxiv.org/abs/2307.07882Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2307.07882Direct 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/2307.07882Direct OA link when available
- Concepts
-
Tikhonov regularization, Artificial neural network, Computer science, Backpropagation, Inversion (geology), Ode, Inverse problem, Regularization (linguistics), Optimal control, Ordinary differential equation, Gradient descent, Identification (biology), Mathematical optimization, Artificial intelligence, Mathematics, Applied mathematics, Differential equation, Biology, Paleontology, Botany, Structural basin, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 3, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.effectiveness | 45 |
| abstract_inverted_index.gradient-free | 24 |
| abstract_inverted_index.gradient-based | 112 |
| abstract_inverted_index.identification | 58, 95 |
| abstract_inverted_index.regularization | 77 |
| abstract_inverted_index.backpropagation, | 20 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5088737112 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 1 |
| citation_normalized_percentile |