Neural Network Emulation of Flow in Heavy-Ion Collisions at Intermediate Energies Article Swipe
Nicholas J. Cox
,
Xavier Grundler
,
Bao-An Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.18421
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.18421
Applications of new techniques in machine learning are speeding up progress in research in various fields. In this work, we construct and evaluate a deep neural network (DNN) to be used within a Bayesian statistical framework as a faster and more reliable alternative to the Gaussian Process (GP) emulator of an isospin-dependent Boltzmann-Uehling-Uhlenbeck (IBUU) transport model simulator of heavy-ion reactions at intermediate beam energies. We found strong evidence of DNN being able to emulate the IBUU simulator's prediction on the strengths of protons' directed and elliptical flow very efficiently even with small training datasets and with accuracy about ten times higher than the GP. Limitations of our present work and future improvements are also discussed.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2406.18421
- https://arxiv.org/pdf/2406.18421
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400104885
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4400104885Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2406.18421Digital Object Identifier
- Title
-
Neural Network Emulation of Flow in Heavy-Ion Collisions at Intermediate EnergiesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-26Full publication date if available
- Authors
-
Nicholas J. Cox, Xavier Grundler, Bao-An LiList of authors in order
- Landing page
-
https://arxiv.org/abs/2406.18421Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2406.18421Direct 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/2406.18421Direct OA link when available
- Concepts
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Emulation, Heavy ion, Flow (mathematics), Ion, Artificial neural network, Physics, Computer science, Environmental science, Mechanics, Artificial intelligence, Economics, Quantum mechanics, Economic growthTop 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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