Julie Bessac

According to our database, Julie Bessac authored at least 17 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Online learning of quadratic manifolds from streaming data for nonlinear dimensionality reduction and nonlinear model reduction.

Paul Schwerdtner, Prakash Mohan, Aleksandra Pachalieva, Julie Bessac, Daniel O'Malley, Benjamin Peherstorfer

CoRR, 2024

Uncertainty Visualization Challenges in Decision Systems with Ensemble Data & Surrogate Models.

Sam Molnar, J. D. Laurence-Chasen, Yuhan Duan, Julie Bessac, Kristi Potter

Proceedings of the IEEE Workshop on Uncertainty Visualization: Applications, 2024

2023

Neural networks for parameter estimation in intractable models.

Amanda Lenzi, Julie Bessac, Johann Rudi, Michael L. Stein

Comput. Stat. Data Anal., September, 2023

Black-box statistical prediction of lossy compression ratios for scientific data.

Robert Underwood, Julie Bessac, David Krasowska, Jon C. Calhoun, Sheng Di, Franck Cappello

Int. J. High Perform. Comput. Appl., July, 2023

A Lightweight, Effective Compressibility Estimation Method for Error-bounded Lossy Compression.

Arkaprabha Ganguli, Robert Underwood, Julie Bessac, David Krasowska, Jon C. Calhoun, Sheng Di, Franck Cappello

Proceedings of the IEEE International Conference on Cluster Computing, 2023

2022

Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification.

Daniel Getter, Julie Bessac, Johann Rudi, Yan Feng

CoRR, 2022

Understanding the Effects of Modern Compressors on the Community Earth Science Model.

Robert Underwood, Julie Bessac, Sheng Di, Franck Cappello

Proceedings of the 8th IEEE/ACM International Workshop on Data Analysis and Reduction for Big Scientific Data, 2022

2021

Power grid frequency prediction using spatiotemporal modeling.

Amanda Lenzi, Julie Bessac, Mihai Anitescu

Stat. Anal. Data Min., 2021

Online data analysis and reduction: An important Co-design motif for extreme-scale computers.

Ian T. Foster, Mark Ainsworth, Julie Bessac, Franck Cappello, Jong Choi, Sheng Di, Zichao Wendy Di, Ali Murat Gok, Hanqi Guo, Kevin A. Huck, Christopher Kelly, Scott Klasky, Kerstin Kleese van Dam, Xin Liang, Kshitij Mehta, Manish Parashar, Tom Peterka, Line Pouchard, Tong Shu, Ozan Tugluk, Hubertus Van Dam, Lipeng Wan, Matthew Wolf, Justin M. Wozniak, Wei Xu, Igor Yakushin, Shinjae Yoo, Todd S. Munson

Int. J. High Perform. Comput. Appl., 2021

Exploring Lossy Compressibility through Statistical Correlations of Scientific Datasets.

David Krasowska, Julie Bessac, Robert Underwood, Jon C. Calhoun, Sheng Di, Franck Cappello

Proceedings of the 2021 7th International Workshop on Data Analysis and Reduction for Big Scientific Data, 2021

Parameter Estimation with Dense and Convolutional Neural Networks Applied to the FitzHugh-Nagumo ODE.

Johann Rudi, Julie Bessac, Amanda Lenzi

Proceedings of the Mathematical and Scientific Machine Learning, 2021

2020

Statistical Treatment of Inverse Problems Constrained by Differential Equations-Based Models with Stochastic Terms.

Emil M. Constantinescu, Noémi Petra, Julie Bessac, Cosmin G. Petra

SIAM/ASA J. Uncertain. Quantification, 2020

SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors.

Kai Zhao, Sheng Di, Xin Liang, Sihuan Li, Dingwen Tao, Julie Bessac, Zizhong Chen, Franck Cappello

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

Image Segmentation for Dust Detection Using Semi-supervised Machine Learning.

Manzhu Yu, Julie Bessac, Ling Xu, Aryya Gangopadhyay, Yingxi Rona Shi, Jianwu Wang

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019

Global Sensitivity Analysis for Statistical Model Parameters.

Joseph L. Hart, Julie Bessac, Emil M. Constantinescu

SIAM/ASA J. Uncertain. Quantification, 2019

2018

Efficient computation of derivatives for solving optimization problems in R and Python using SWIG-generated interfaces to ADOL-C.

Kshitij Kulshreshtha, Sri Hari Krishna Narayanan, Julie Bessac, K. Macintyre

Optim. Methods Softw., 2018

2017

Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales.

Ian T. Foster, Mark Ainsworth, Bryce Allen, Julie Bessac, Franck Cappello, Jong Youl Choi, Emil M. Constantinescu, Philip E. Davis, Sheng Di, Zichao Wendy Di, Hanqi Guo, Scott Klasky, Kerstin Kleese van Dam, Tahsin M. Kurç, Qing Liu, Abid Malik, Kshitij Mehta, Klaus Mueller, Todd S. Munson, George Ostrouchov, Manish Parashar, Tom Peterka, Line Pouchard, Dingwen Tao, Ozan Tugluk, Stefan M. Wild, Matthew Wolf, Justin M. Wozniak, Wei Xu, Shinjae Yoo

Proceedings of the Euro-Par 2017: Parallel Processing - 23rd International Conference on Parallel and Distributed Computing, Santiago de Compostela, Spain, August 28, 2017