Disentangled Multi-Fidelity Deep Bayesian Active Learning Article Swipe
Dongxia Wu
,
Ruijia Niu
,
Matteo Chinazzi
,
Yi-An Ma
,
Rose Yu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.04392
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2305.04392
To balance quality and cost, various domain areas of science and engineering run simulations at multiple levels of sophistication. Multi-fidelity active learning aims to learn a direct mapping from input parameters to simulation outputs at the highest fidelity by actively acquiring data from multiple fidelity levels. However, existing approaches based on Gaussian processes are hardly scalable to high-dimensional data. Deep learning-based methods often impose a hierarchical structure in hidden representations, which only supports passing information from low-fidelity to high-fidelity. These approaches can lead to the undesirable propagation of errors from low-fidelity representations to high-fidelity ones. We propose a novel framework called Disentangled Multi-fidelity Deep Bayesian Active Learning (D-MFDAL), which learns the surrogate models conditioned on the distribution of functions at multiple fidelities. On benchmark tasks of learning deep surrogates of partial differential equations including heat equation, Poisson's equation and fluid simulations, our approach significantly outperforms state-of-the-art in prediction accuracy and sample efficiency.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2305.04392
- https://arxiv.org/pdf/2305.04392
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4375958696
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4375958696Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2305.04392Digital Object Identifier
- Title
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Disentangled Multi-Fidelity Deep Bayesian Active LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-07Full publication date if available
- Authors
-
Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yi-An Ma, Rose YuList of authors in order
- Landing page
-
https://arxiv.org/abs/2305.04392Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2305.04392Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2305.04392Direct OA link when available
- Concepts
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Fidelity, Computer science, Benchmark (surveying), Artificial intelligence, Deep learning, Scalability, Machine learning, High fidelity, Bayesian probability, Engineering, Geography, Electrical engineering, Geodesy, Database, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.high-dimensional | 57 |
| abstract_inverted_index.representations, | 69 |
| abstract_inverted_index.state-of-the-art | 145 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |