Efficient Learning of Accurate Surrogates for Simulations of Complex Systems Article Swipe
A. Diaw
,
Michael McKerns
,
Irina Sagert
,
Liam Stanton
,
Michael S. Murillo
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.2172/2377984
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.2172/2377984
Machine learning methods are increasingly deployed to construct surrogate models for complex physical systems at a reduced computational cost. However, the predictive capability of these surrogates degrades in the presence of noisy, sparse, or dynamic data. We introduce an online learning method empowered by optimizer-driven sampling that has two advantages over current approaches: it ensures that all local extrema (including endpoints) of the model response surface are included in the training data and it employs a continuous validation and update process in which surrogates undergo retraining when their performance falls below a validity threshold. We find, using benchmark functions, that optimizer-directed sampling generally outperforms traditional sampling methods in terms of accuracy around local extrema even when the scoring metric is biased towards assessing overall accuracy. Finally, the application to dense nuclear matter demonstrates that highly accurate surrogates for a nuclear equation of state model can be reliably auto-generated from expensive calculations using few model evaluations.
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- Type
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- Language
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- https://doi.org/10.2172/2377984
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- 10
- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4400174148Canonical identifier for this work in OpenAlex
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https://doi.org/10.2172/2377984Digital Object Identifier
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Efficient Learning of Accurate Surrogates for Simulations of Complex SystemsWork title
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reportOpenAlex work type
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enPrimary language
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2021Year of publication
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2021-11-05Full publication date if available
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A. Diaw, Michael McKerns, Irina Sagert, Liam Stanton, Michael S. MurilloList of authors in order
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https://doi.org/10.2172/2377984Publisher landing page
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://www.osti.gov/biblio/2377984Direct OA link when available
- Concepts
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Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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