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Trigger Detection for Adaptive Scientific Workflows Using Percentile Sampling., , , , , and . SIAM J. Sci. Comput., (2016)Optimal Compressed Sensing and Reconstruction of Unstructured Mesh Datasets., , , , , , , , , and . Data Sci. Eng., 3 (1): 1-23 (2018)Tracking features in embedded surfaces: Understanding extinction in turbulent combustion., , , , , , and . LDAV, page 9-16. IEEE Computer Society, (2015)A priori DNS evaluation of the shadow-position mixing model in turbulent reactive flows, , , , and . Bulletin of the American Physical Society, San Francisco, CA, (November 2014)An a priori DNS study of the shadow-position mixing model, , , , and . Combustion and Flame, (January 2016)Enabling Adaptive Scientific Workflows Via Trigger Detection., , , , and . ISAV@SC, page 41-45. ACM, (2015)Trigger detection for adaptive scientific workflows using percentile sampling., , , , , and . CoRR, (2015)Principal strain-rate transport in turbulent non-premixed DME flames: a comparative study using DNS and tomographic particle image velocimetry, , , and . 12th Turbulent Nonpremixed Flame Workshop, Pleasanton, CA, (2014)poster presentation.Assessment of high-resolution methods for numerical simulations of compressible turbulence with shock waves., , , , , , , , , and 2 other author(s). J. Comput. Phys., 229 (4): 1213-1237 (2010)A modified artificial viscosity approach for compressible turbulence simulations., and . J. Comput. Phys., 228 (14): 4965-4969 (2009)