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Constructing a Simulation Surrogate with Partially Observed Output., , and . Technometrics, 66 (1): 1-13 (2024)Emerging Frameworks for Advancing Scientific Workflows Research, Development, and Education., , , , , , , , , and 7 other author(s). WORKS, page 74-80. IEEE, (2021)DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks., , , , and . HiPC, page 42-51. IEEE, (2018)Visualizing and Improving the Robustness of Phase Retrieval Algorithms., , , and . ICCS, volume 51 of Procedia Computer Science, page 815-824. Elsevier, (2015)Benchmarking Derivative-Free Optimization Algorithms, and . SIAM Journal on Optimization, 20 (1): 172--191 (2009)Active-learning-based surrogate models for empirical performance tuning., , and . CLUSTER, page 1-8. IEEE Computer Society, (2013)Exploiting Performance Portability in Search Algorithms for Autotuning., , , and . IPDPS Workshops, page 1535-1544. IEEE Computer Society, (2016)Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales., , , , , , , , , and 20 other author(s). Euro-Par, volume 10417 of Lecture Notes in Computer Science, page 3-19. Springer, (2017)ACCOLADES: A Scalable Workflow Framework for Large-Scale Simulation and Analyses of Automotive Engines., and . ISC, volume 9137 of Lecture Notes in Computer Science, page 87-95. Springer, (2015)Calibrating Sensing Drift in Tomographic Inversion., , and . ICIP, page 1267-1271. IEEE, (2019)