In-Situ Spatial Inference on Climate Simulations with Sparse Gaussian Processes-Reference-Cited by-同舟云学术

In-Situ Spatial Inference on Climate Simulations with Sparse Gaussian Processes

Published:2021-11-15 Issue: Volume: Page:
ISSN:
Container-title:ISAV'21: In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization
language:
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Author:

Grosskopf Michael¹,Lawrence Earl¹,Biswas Ayan¹,Tang Li¹,Rumsey Kelin¹,Van Roekel Luke¹,Urban Nathan²

Affiliation:

1. Los Alamos National Laboratory, USA

2. Brookhaven National Laboratory, USA

Funder

Laboratory Directed Research and Development

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3490138.3490140

Reference36 articles.

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2. SIAM review 59, 1;Bezanson Jeff,2017

3. Christopher M Bishop. 2006. Pattern recognition. Machine Learning 128(2006). Christopher M Bishop. 2006. Pattern recognition. Machine Learning 128(2006).

4. A. Biswas S. Dutta E. Lawrence J. Patchett J. C. Calhoun and J. Ahrens. 2020. Probabilistic Data-Driven Sampling via Multi-Criteria Importance Analysis. IEEE Transactions on Visualization and Computer Graphics (2020) 1–1. A. Biswas S. Dutta E. Lawrence J. Patchett J. C. Calhoun and J. Ahrens. 2020. Probabilistic Data-Driven Sampling via Multi-Criteria Importance Analysis. IEEE Transactions on Visualization and Computer Graphics (2020) 1–1.

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