https://hal.archives-ouvertes.fr/hal-02052839Soize, ChristianChristianSoizeMSME - Laboratoire de Modélisation et Simulation Multi Echelle - UPEM - Université Paris-Est Marne-la-Vallée - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12 - CNRS - Centre National de la Recherche ScientifiqueGhanem, RogerRogerGhanemUSC - University of Southern CaliforniaSafta, CosminCosminSaftaSandia National Laboratories [Livermore] - Sandia National Laboratories - CorporationHuan, XunXunHuanSandia National Laboratories [Livermore] - Sandia National Laboratories - CorporationVane, ZacharyZacharyVaneSandia National Laboratories [Livermore] - Sandia National Laboratories - CorporationOefelein, JosephJosephOefeleinGA TECH - Daniel Guggenheim School of Aerospace Engineering - Georgia Institute of Technology [Atlanta]Sandia National Laboratories [Livermore] - Sandia National Laboratories - CorporationLacaze, GuilhemGuilhemLacazeSandia National Laboratories [Livermore] - Sandia National Laboratories - CorporationNajm, HabibHabibNajmSandia National Laboratories [Livermore] - Sandia National Laboratories - CorporationEnhancing model predictability for a scramjet using probabilistic learning on manifoldsHAL CCSD2019[SPI] Engineering Sciences [physics][MATH.MATH-PR] Mathematics [math]/Probability [math.PR][STAT] Statistics [stat][STAT.ML] Statistics [stat]/Machine Learning [stat.ML]Soize, Christian2019-02-28 17:30:112022-09-29 14:21:152019-03-07 09:40:41enJournal articleshttps://hal.archives-ouvertes.fr/hal-02052839/document10.2514/1.J057069application/pdf1The computational burden of Large-eddy Simulation for reactive flows is exacerbated in the presence of uncertainty in flow conditions or kinetic variables. A comprehensive statistical analysis, with a sufficiently large number of samples, remains elusive. Statistical learning is an approach that allows for extracting more information using fewer samples. Such procedures, if successful, would greatly enhance the predictability of models in the sense of improving exploration and characterization of uncertainty due to model error and input dependencies, all while being constrained by the size of the associated statistical samples. In this paper, we show how a recently developed procedure for probabilistic learning on manifolds can serve to improve the predictability in a probabilistic framework of a scramjet simulation. The estimates of the probability density functions of the quantities of interest are improved together with estimates of the statistics of their maxima. We also demonstrate how the improved statistical model adds critical insight to the performance of the model.