Nonlinear Assimilation via Score-based Sequential Langevin Sampling Article Swipe
Zhao Ding
,
Chenguang Duan
,
Yuling Jiao
,
Jerry Zhijian Yang
,
Cheng Yuan
,
Pingwen Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.13443
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.13443
This paper presents score-based sequential Langevin sampling (SSLS), a novel approach to nonlinear data assimilation within a recursive Bayesian filtering framework. The proposed method decomposes the assimilation process into alternating prediction and update steps, leveraging dynamic models for state prediction while incorporating observational data through score-based Langevin Monte Carlo during updates. To address challenges in posterior sampling, we introduce an annealing strategy within the update mechanism. We provide theoretical guarantees for SSLS convergence in total variation (TV) distance under certain conditions, providing insights into error behavior with respect to key hyper-parameters. Our numerical experiments across challenging scenarios -- including high-dimensional systems, strong nonlinearity, and sparse observations -- demonstrate the robust performance of the proposed method. Furthermore, SSLS effectively quantifies the uncertainty associated with the estimated states, making it particularly valuable for the error calibration.
Related Topics
Concepts
Nonlinear system
Assimilation (phonology)
Sampling (signal processing)
Sequential sampling
Data assimilation
Langevin dynamics
Computer science
Statistical physics
Econometrics
Statistics
Mathematics
Artificial intelligence
Algorithm
Physics
Meteorology
Philosophy
Computer vision
Spatial distribution
Quantum mechanics
Linguistics
Filter (signal processing)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2411.13443
- https://arxiv.org/pdf/2411.13443
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404649737
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404649737Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2411.13443Digital Object Identifier
- Title
-
Nonlinear Assimilation via Score-based Sequential Langevin SamplingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-20Full publication date if available
- Authors
-
Zhao Ding, Chenguang Duan, Yuling Jiao, Jerry Zhijian Yang, Cheng Yuan, Pingwen ZhangList of authors in order
- Landing page
-
https://arxiv.org/abs/2411.13443Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2411.13443Direct 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/2411.13443Direct OA link when available
- Concepts
-
Nonlinear system, Assimilation (phonology), Sampling (signal processing), Sequential sampling, Data assimilation, Langevin dynamics, Computer science, Statistical physics, Econometrics, Statistics, Mathematics, Artificial intelligence, Algorithm, Physics, Meteorology, Philosophy, Computer vision, Spatial distribution, Quantum mechanics, Linguistics, Filter (signal processing)Top 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.numerical | 92 |
| abstract_inverted_index.posterior | 55 |
| abstract_inverted_index.providing | 81 |
| abstract_inverted_index.recursive | 17 |
| abstract_inverted_index.sampling, | 56 |
| abstract_inverted_index.scenarios | 96 |
| abstract_inverted_index.variation | 75 |
| abstract_inverted_index.associated | 121 |
| abstract_inverted_index.challenges | 53 |
| abstract_inverted_index.decomposes | 24 |
| abstract_inverted_index.framework. | 20 |
| abstract_inverted_index.guarantees | 69 |
| abstract_inverted_index.leveraging | 34 |
| abstract_inverted_index.mechanism. | 65 |
| abstract_inverted_index.prediction | 30, 39 |
| abstract_inverted_index.quantifies | 118 |
| abstract_inverted_index.sequential | 4 |
| abstract_inverted_index.alternating | 29 |
| abstract_inverted_index.challenging | 95 |
| abstract_inverted_index.conditions, | 80 |
| abstract_inverted_index.convergence | 72 |
| abstract_inverted_index.demonstrate | 107 |
| abstract_inverted_index.effectively | 117 |
| abstract_inverted_index.experiments | 93 |
| abstract_inverted_index.performance | 110 |
| abstract_inverted_index.score-based | 3, 45 |
| abstract_inverted_index.theoretical | 68 |
| abstract_inverted_index.uncertainty | 120 |
| abstract_inverted_index.Furthermore, | 115 |
| abstract_inverted_index.assimilation | 14, 26 |
| abstract_inverted_index.calibration. | 133 |
| abstract_inverted_index.observations | 105 |
| abstract_inverted_index.particularly | 128 |
| abstract_inverted_index.incorporating | 41 |
| abstract_inverted_index.nonlinearity, | 102 |
| abstract_inverted_index.observational | 42 |
| abstract_inverted_index.high-dimensional | 99 |
| abstract_inverted_index.hyper-parameters. | 90 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |