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ORCIDJuliane Mueller 0002
Person information
- affiliation: National Renewable Energy Laboratory, Golden, CO, USA
- affiliation (former): Lawrence Berkeley National Laboratory, CA, USA
Other persons with the same name
- Juliane Müller 0001
![[0000-0002-4966-8285]](https://dblp1.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-4966-8285")
— Technische Universität Dresden, Germany - Juliane Müller-Sielaff (aka: Juliane Müller 0003) — Otto von Guericke University Magdeburg, Magdeburg, Germany
- Juliane Müller 0004 — Cornell University, Ithaca, NY, USA
- Juliane Müller 0005 — University of Rostock, Germany
- Juliane Müller 0006 — Bundeswehr University Munich, Institute of Flight Systems, Neubiberg, Germany
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Books and Theses
- 2012
[b1]Juliane Müller:
Surrogate Model Algorithms for Computationally Expensive Black-Box Global Optimization Problems. University of Tampere, Finland, 2012
Journal Articles
- 2024
- [j10]Yiming Che, Juliane Müller, Changqing Cheng:
Dispersion-enhanced sequential batch sampling for adaptive contour estimation. Qual. Reliab. Eng. Int. 40(1): 131-144 (2024) - 2023
- [j9]Jangho Park, Juliane Müller, Bhavna Arora, Boris Faybishenko, Gilberto Zonta Pastorello, Charuleka Varadharajan, Reetik Sahu, Deborah A. Agarwal:
Long-term missing value imputation for time series data using deep neural networks. Neural Comput. Appl. 35(12): 9071-9091 (2023) - 2021
- [j8]Juliane Müller, Boris Faybishenko, Deborah A. Agarwal, Stephen Bailey, Chongya Jiang, Youngryel Ryu, Craig Tull, Lavanya Ramakrishnan:
Assessing data change in scientific datasets. Concurr. Comput. Pract. Exp. 33(16) (2021) - [j7]Anthony P. Austin, Mohan Krishnamoorthy, Sven Leyffer, Stephen Mrenna, Juliane Müller, Holger Schulz:
Practical algorithms for multivariate rational approximation. Comput. Phys. Commun. 261: 107663 (2021) - [j6]Juliane Müller, Jangho Park, Reetik Sahu, Charuleka Varadharajan, Bhavna Arora, Boris Faybishenko, Deborah A. Agarwal:
Surrogate optimization of deep neural networks for groundwater predictions. J. Glob. Optim. 81(1): 203-231 (2021) - 2020
- [j5]Juliane Müller:
An algorithmic framework for the optimization of computationally expensive bi-fidelity black-box problems. INFOR Inf. Syst. Oper. Res. 58(2): 264-289 (2020) - 2019
- [j4]Juliane Müller, Marcus Day:
Surrogate Optimization of Computationally Expensive Black-Box Problems with Hidden Constraints. INFORMS J. Comput. 31(4): 689-702 (2019) - 2017
- [j3]Juliane Müller:
SOCEMO: Surrogate Optimization of Computationally Expensive Multiobjective Problems. INFORMS J. Comput. 29(4): 581-596 (2017) - [j2]Juliane Müller, Joshua D. Woodbury:
GOSAC: global optimization with surrogate approximation of constraints. J. Glob. Optim. 69(1): 117-136 (2017) - 2010
- [j1]Juliane Müller:
Approximative solutions to the bicriterion Vehicle Routing Problem with Time Windows. Eur. J. Oper. Res. 202(1): 223-231 (2010)
Conference and Workshop Papers
- 2022
- [c4]Juliane Müller, Wim Lavrijsen, Costin Iancu, Wibe de Jong:
Accelerating Noisy VQE Optimization with Gaussian Processes. QCE 2022: 215-225 - 2021
- [c3]Vincent Dumont, Casey Garner, Anuradha Trivedi, Chelsea Jones, Vidya Ganapati, Juliane Mueller, Talita Perciano, Mariam Kiran, Marc Day:
HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization. MLHPC@SC 2021: 81-93 - 2020
- [c2]Wim Lavrijsen, Ana Tudor, Juliane Müller, Costin Iancu, Wibe de Jong:
Classical Optimizers for Noisy Intermediate-Scale Quantum Devices. QCE 2020: 267-277 - 2017
- [c1]Erich Lohrmann, Zarija Lukic, Dmitriy Morozov, Juliane Müller:
Programmable In Situ System for Iterative Workflows. JSSPP 2017: 122-131
Informal and Other Publications
- 2023
- [i10]Luka Grbcic, Juliane Müller, Wibe Albert de Jong:
Efficient Inverse Design Optimization through Multi-fidelity Simulations, Machine Learning, and Search Space Reduction Strategies. CoRR abs/2312.03654 (2023) - 2022
- [i9]Jangho Park, Juliane Müller, Bhavna Arora, Boris Faybishenko, Gilberto Zonta Pastorello, Charuleka Varadharajan, Reetik Sahu, Deborah A. Agarwal:
Long-Term Missing Value Imputation for Time Series Data Using Deep Neural Networks. CoRR abs/2202.12441 (2022) - [i8]Vincent Dumont, Xiangyang Ju, Juliane Mueller:
Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations. CoRR abs/2208.07715 (2022) - [i7]Rey Mendoza, Minh Nguyen, Judith Weng Zhu, Vincent Dumont, Talita Perciano, Juliane Mueller, Vidya Ganapati:
A Self-Supervised Approach to Reconstruction in Sparse X-Ray Computed Tomography. CoRR abs/2211.00002 (2022) - 2021
- [i6]Wenjing Wang, Mohan Krishnamoorthy, Juliane Müller, Stephen Mrenna, Holger Schulz, Xiangyang Ju, Sven Leyffer, Zachary Marshall:
BROOD: Bilevel and Robust Optimization and Outlier Detection for Efficient Tuning of High-Energy Physics Event Generators. CoRR abs/2103.05751 (2021) - [i5]Vincent Dumont, Casey Garner, Anuradha Trivedi, Chelsea Jones, Vidya Ganapati, Juliane Mueller, Talita Perciano, Mariam Kiran, Marc Day:
HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization. CoRR abs/2110.01698 (2021) - [i4]Luca Pion-Tonachini, Kristofer E. Bouchard, Héctor García Martín, Sean Peisert, W. Bradley Holtz, Anil Aswani, Dipankar Dwivedi, Haruko M. Wainwright, Ghanshyam Pilania, Benjamin Nachman, Babetta L. Marrone, Nicola Falco, Prabhat, Daniel B. Arnold, Alejandro Wolf-Yadlin, Sarah Powers, Sharlee Climer, Quinn Jackson, Ty Carlson, Michael Sohn, Petrus H. Zwart, Neeraj Kumar, Amy Justice, Claire J. Tomlin, Daniel A. Jacobson, Gos Micklem, Georgios V. Gkoutos, Peter J. Bickel, Jean-Baptiste Cazier, Juliane Müller, Bobbie-Jo Webb-Robertson, Rick Stevens, Mark Anderson, Kenneth Kreutz-Delgado, Michael W. Mahoney, James B. Brown:
Learning from learning machines: a new generation of AI technology to meet the needs of science. CoRR abs/2111.13786 (2021) - 2019
- [i3]Juliane Müller, Jangho Park, Reetik Sahu, Charuleka Varadharajan, Bhavna Arora, Boris Faybishenko, Deborah A. Agarwal:
Surrogate Optimization of Deep Neural Networks for Groundwater Predictions. CoRR abs/1908.10947 (2019) - [i2]Anthony P. Austin, Mohan Krishnamoorthy, Sven Leyffer, Stephen Mrenna, Juliane Müller, Holger Schulz:
Multivariate Rational Approximation. CoRR abs/1912.02272 (2019) - 2018
- [i1]Timur Takhtaganov, Juliane Müller:
Adaptive Gaussian process surrogates for Bayesian inference. CoRR abs/1809.10784 (2018)
Coauthor Index
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