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• Williams, S.D. and Curry, D.M., Thermal Protection Materials: Thermophysical Property Data, NASA Reference Publication RP-1289, 1992.
• Stackpoole, M., Sepka, S., Cozmuta, I., and Kontinos, D., Post-Flight Evaluation of Stardus Sample Return Capsule Forebody Heatshield Material, J. Thermophys. Heat Transf., 24(4):694–707, 2010.
• Wright, M., Cozmuta, I., Laub, B., Chen, Y.K., and Wilcoxson, W.H., Defining Ablative Thermal Protection System Margins for Planetary Entry Vehicles, 42nd AIAA Thermophysics Conf., American Institute of Aeronautics and Astronautics, 2011.
• Seedhouse, E., Dragon Design, Development, and Test, in SpaceX’s Dragon: America’s Next Generation Spacecraft, Chichester, UK: Springer, pp. 23–44, 2016.
• Natali, M., Puri, I., Rallini, M., Kenny, J., and Torre, L., Ablation Modeling of State of the Art EPDM Based Elastomeric Heat Shielding Materials for Solid Rocket Motors, Computat. Mater. Sci., 111:460–480, 2016.
• Reimer, T., Zuber, C., Rieser, J., and Rothermel, T., Determination of the Mechanical Properties of the Lightweight Ablative Material Zuram, in Ceramic Transactions Series, Hoboken, NJ: Wiley, pp. 311–326, 2018.
• Duffa, G., Ablative Thermal Protection Systems Modeling, Reston, VA: AIAA, Inc., 2013.
• Wong, H.W., Peck, J., Assif, J., Panerai, F., Lachaud, J., and Mansour, N.N., Detailed Analysis of Species Production from the Pyrolysis of the Phenolic Impregnated Carbon Ablator, J. Anal. Appl. Pyrolys., 122:258–267, 2016.
• Torres-Herrador, F., Eschenbacher, A., Coheur, J., Blondeau, J., Magin, T.E., and Geem, K.M.V., Decomposition of Carbon/Phenolic Composites for Aerospace Heatshields: Detailed Speciation of Phenolic Resin Pyrolysis Products, Aerospace Sci. Technol., 119:107079–107089, 2021.
• Bessire, B.K. and Minton, T.K., Decomposition of Phenolic Impregnated Carbon Ablator (PICA) as a Function of Temperature and Heating Rate, ACS Appl. Mater. Int., 9(25):21422–21437, 2017.
• Vyazovkin, S., Burnham, A.K., Criado, J.M., Pérez-Maqueda, L.A., Popescu, C., and Sbirrazzuoli, N., ICTAC Kinetics Committee Recommendations for Performing Kinetic Computations on Thermal Analysis Data, Thermochim. Acta, 520(1-2):1–19, 2011.
• Coheur, J., Torres-Herrador, F., Chatelain, P., Mansour, N.N., Magin, T.E., and Arnst, M., Analytical Solution for Multi-Component Pyrolysis Simulations of Thermal Protection Materials, J. Mater. Sci., 56:6845–6860, 2021.
• Torres-Herrador, F., Meurisse, J.B., Panerai, F., Blondeau, J., Lachaud, J., Bessire, B.K., Magin, T.E., and Mansour, N.N., A High Heating Rate Pyrolysis Model for the Phenolic Impregnated Carbon Ablator (PICA) Based on Mass Spectroscopy Experiments, J. Anal. Appl. Pyrol., 141:104625–104635, 2019.
• Najm, H., Berry, R., Safta, C., Sargsyan, K., and Debusschere, B., Data-Free Inference of Uncertain Parameters in Chemical Models, Int. J. Uncertainty Quantif., 4(2):111–132, 2014.
• Cheung, S.H., Miki, K., Prudencio, E., and Simmons, C., Uncertainty Quantification and Robust Predictive System Analysis for High Temperature Kinetics of HCN/O2 /Ar Mixture, Chem. Phys., 475:136–152, 2016.
• Khalil, M. and Najm, H.N., Probabilistic Inference of Reaction Rate Parameters from Summary Statistics, Combust. Theory Model., 22(4):635–665, 2018.
• Urzay, J., Kseib, N., Constantine, P.G., Davidson, D.F., and Iaccarino, G., Uncertainty-Quantifying Models for Chemical-Kinetic Rates, Center for Turbulence Research Annual Briefs, 2012.
• Bruns, M.C., Inferring and Propagating Kinetic Parameter Uncertainty for Condensed Phase Burning Models, Fire Technol., 52(1):93–120, 2015.
• Torres-Herrador, F., Coheur, J., Panerai, F., Magin, T.E., Arnst, M., Mansour, N.N., and Blondeau, J., Competitive Kinetic Model for the Pyrolysis of the Phenolic Impregnated Carbon Ablator, Aerospace Sci. Technol., 100:105784, 2019.
• Beck, J.L. and Katafygiotis, L.S., Updating Models and Their Uncertainties. I: Bayesian Statistical Framework, J. Eng. Mech., 124(4):455–461, 1998.
• Kennedy, M.C. and O’Hagan, A., Bayesian Calibration of Computer Models, J. R. Stat. Soc., 63(3):425–464, 2001.
• Koga, N., A Review of the Mutual Dependence of Arrhenius Parameters Evaluated by the Thermoanalytical Study of Solid-State Reactions: The Kinetic Compensation Effect, Thermochim. Acta, 244:1–20, 1994.
• Galwey, A.K. and Brown, M.E., Thermal Decomposition of Ionic Solids, Amsterdam, The Netherlands: Elsevier, 1999.
• Rodionova, O.E. and Pomerantsev, A.L., Estimating the Parameters of the Arrhenius Equation, Kinet. Catal., 46(3):305–308, 2005.
• Pomerantsev, A.L., Kutsenova, A.V., and Rodionova, O.Y., Kinetic Analysis of Non-Isothermal Solid-State Reactions: Multi-Stage Modeling without Assumptions in the Reaction Mechanism, Phys. Chem. Chem. Phys., 19(5):3606–3615, 2017.
• Soize, C., Construction of Probability Distributions in High Dimension Using the Maximum Entropy Principle: Applications to Stochastic Processes, Random Fields and Random Matrices, Int. J. Numer. Methods Eng., 76(10):1583–1611, 2008.
• Soize, C., Polynomial Chaos Expansion of a Multimodal Random Vector, SIAM/ASA J. Uncertainty Quantif., 3(1):34–60, 2015.
• Soize, C., Uncertainty Quantification, Cham, Switzerland: Springer International Publishing, 2017.
• Kolda, T.G. and Bader, B.W., Tensor Decompositions and Applications, SIAM Rev., 51(3):455–500, 2009.
• Wright, M.J., Beck, R.A.S., Edquist, K.T., Driver, D., Sepka, S.A., Slimko, E.M., and Willcockson, W.H., Sizing and Margins Assessment of Mars Science Laboratory Aeroshell Thermal Protection System, J. Spacecraft Rockets, 51(4):1125–1138, 2014.
• Kendall, R.M., Barlett, E.P., Rindal, R.A., and Moyer, C.B., An Analysis of the Coupled Chemically Reacting Boundary Layer and Charring Ablator: Part I., Tech. Rep., NASA CR-1060, 1968.
• Chen, Y.K. and Milos, F.S., Ablation and Thermal Response Program for Spacecraft Heatshield Analysis, J. Spacecraft Rockets, 36(3):475–483, 1999.
• Sykes, G.F., Decomposition Characteristics of a Char-Forming Phenolic Polymer Used for Ablative Composites, Tech. Rep., NASA TN D-3810, National Aeronautics and Space Administration, 1967.
• Goldstein, H.E., Pyrolysis Kinetics of Nylon 6-6, Phenolic Resin, and Their Composites, J. Macromolec. Sci., 3(4):649–673, 1969.
• Trick, K.A. and Saliba, T.E., Mechanisms of the Pyrolysis of Phenolic Resin in a Carbon/Phenolic Composite, Carbon, 33(11):1509–1515, 1995.
• Trick, K.A., Saliba, T.E., and Sandhu, S.S., A Kinetic Model of the Pyrolysis of Phenolic Resin in a Carbon/Phenolic Composite, Carbon, 35(3):393–401, 1997.
• Wong, H.W., Peck, J., Bonomi, R.E., Assif, J., Panerai, F., Reinisch, G., Lachaud, J., and Mansour, N.N., Quantitative Determination of Species Production from Phenol-Formaldehyde Resin Pyrolysis, Polymer Degradat. Stab., 112:122–131, 2015.
• Bessire, B.K., Lahankar, S.A., and Minton, T.K., Pyrolysis of Phenolic Impregnated Carbon Ablator (PICA), ACS Appl. Mater. Inter., 7(3):1383–1395, 2015.
• Lachaud, J., Magin, T.E., Cozmuta, I., and Mansour, N.N., A Short Review of Ablative-Material Response Models and Simulation Tools, 7th European Symposium on Aerothermodynamics, L. Ouwehand, Ed., Brugge, Belgium, ESTEC-ESA, SP-692, pp. 91–98, 2011.
• Arnst, M., Álvarez, B.A., Ponthot, J.P., and Boman, R., Itô-SDE MCMC Method for Bayesian Characterization of Errors Associated with Data Limitations in Stochastic Expansion Methods for Uncertainty Quantification, J. Comput. Phys., 349:59– 79, 2017.
• Arnst, M. and Soize, C., Identification and Sampling of Bayesian Posteriors of High-Dimensional Symmetric Positive-Definite Matrices for Data-Driven Updating of Computational Models, Comput. Methods Appl. Mech. Eng., 352:300–323, 2019.
• Girolami, M. and Calderhead, B., Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods, J. R. Stat. Soc., 73(2):123–214, 2011.
• Law, K., Proposals Which Speed Up Function-Space MCMC, J. Comput. Appl. Math., 262:127–138, 2014.
• Hoffman, M.D. and Gelman, A., The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo, J. Mach. Learn. Res., 15:1593–1623, 2014.
• Marshall, T. and Roberts, G., An Adaptive Approach to Langevin MCMC, Stat. Comput., 22(5):1041–1057, 2011.
• Atchadé, Y.F., An Adaptive Version for the Metropolis Adjusted Langevin Algorithm with a Truncated Drift, Methodol. Comput. Appl. Probab., 8(2):235–254, 2006.
• Haario, H., Saksman, E., and Tamminen, J., An Adaptive Metropolis Algorithm, Bernoulli, 7(2):223–242, 2001.
• Atchadé, Y.F. and Rosenthal, J.S., On Adaptive Markov Chain Monte Carlo Algorithms, Bernoulli, 11(5):815–828, 2005.
• Andrieu, C. and Thoms, J., A Tutorial on Adaptive MCMC, Stat. Comput., 18:343–374, 2008.
• Roberts, G.O. and Rosenthal, J.S., Examples of Adaptive MCMC, J. Comput. Graph. Stat., 18(2):349–367, 2009.
• Rosenthal, J.S., Optimal Proposal Distributions and Adaptive MCMC, Boca Raton, FL: Chapman and Hall, 2010.
• Gelman, A.G., Roberts, G.O., and Gilks, W.R., Efficient Metropolis Jumping Rules, Oxford, UK: Oxford University Press, pp. 599–608, 1996.
• Andrieu, C. and Moulines, É., On the Ergodicity Properties of Some Adaptive MCMC Algorithms, Annals Appl. Probab., 16(3):1462–1505, 2006.
• Coheur, Uncertainty Quantification of Aerothermal Flow-Material Simulations of Low-Density Ablative Thermal Protection Systems, PhD, University of Liege, University of Louvain, 2021.
• Hairer, E., Lubich, C., and Wanner, G., Geometric Numerical Integration, Berlin, Germany: Springer-Verlag, 2006.
• Guilleminot, J. and Soize, C., Itô SDE–Based Generator for a Class of Non-Gaussian Vector-Valued Random Fields in Uncertainty Quantification, SIAM J. Sci. Comput., 36(6):A2763–A2786, 2014.
• Lachaud, J. and Mansour, N.N., Porous-Material Analysis Toolbox Based on OpenFOAM and Applications, J. Thermophys. Heat Transf., 28(2):191–202, 2014.
• Scoggins, J.B., Leroy, V., Bellas-Chatzigeorgis, G., Dias, B., and Magin, T.E., Mutation++: MUlticomponent Thermodynamic and Transport Properties for IONized Gases in C++, SoftwareX, 12:100575–100583, 2020.
• Meurisse, J., Lachaud, J., Panerai, F., Tang, C., and Mansour, N.N., Multidimensional Material Response Simulations of a Full-Scale Tiled Ablative Heatshield, Aerospace Sci. Technol., 76:497–511, 2018.
• Lachaud, J., Martin, A., Van Eekelen, T., and Cozmuta, I., Ablationtest-Caseseries#2, 5th Ablation Workshop, Lexington, Kentucky, KY, pp. 10–18, 2012.
• Edquist, K., Dyakonov, A., Wright, M., and Tang, C., Aerothermodynamic Design of the Mars Science Laboratory Heatshield, 41st AIAA Thermophysics Conf., AIAA, 2009.
• White, T.R., Mahzari, M., Bose, D., and Santos, J.A., Post-Flight Analysis of Mars Science Laboratory’s Entry Aerothermal Environment and Thermal Protection System Response, 44th AIAA Thermophysics Conf., AIAA, 2013.
• Lachaud, J., Scoggins, J., Magin, T., Meyer, M., and Mansour, N., A Generic Local Thermal Equilibrium Model for Porous Reactive Materials Submitted to High Temperatures, Int. J. Heat Mass Transf., 108:1406–1417, 2017.
• Griewank, A. and Walther, A., Evaluating Derivatives, Philadelphia, PA: SIAM, 2008.
• Cao, Y., Li, S., Petzold, L., and Serban, R., Adjoint Sensitivity Analysis for Differential-Algebraic Equations: The Adjoint DAE System and Its Numerical Solution, SIAM J. Sci. Comput., 24(3):21, 2003.
• Bosco, A., Bayesian Inference for the Identification of Model Parameters in Atmospheric Entry Problems, Master’s, University of Liège, 2019.
• Dodwell, T.J., Ketelsen, C., Scheichl, R., and Teckentrup, A.L., A Hierarchical Multilevel Markov Chain Monte Carlo Algorithm with Applications to Uncertainty Quantification in Subsurface Flow, SIAM/ASA J. Uncertainty Quantif., 3(1):1075– 1108, 2015.
• Beskos, A., Jasra, A., Law, K., Tempone, R., and Zhou, Y., Multilevel Sequential Monte Carlo Samplers, Stochastic Proc. Their Appl., 127(5):1417–1440, 2017.
• Heng, J., Jasra, A., Law, K.J.H., and Tarakanov, A., On Unbiased Estimation for Discretized Models, Stat. Comput., arXiv:2102.12230, 2021.
• Jasra, A., Law, K.J.H., and Lu, D., Unbiased Estimation of the Gradient of the Log-Likelihood in Inverse Problems, Stat. Comput., 31(3):21, 2021.
• Burnham, K.P. and Anderson, D.R., Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd ed., New York: Springer, 2002.
• MacKay, D., Information Theory, Inference, and Learning Algorithms, Cambridge, UK: Cambridge University Press, 2003.
• Link, W.A. and Barker, R.J., Model Weights and the Foundations of Multimodel Inference, Ecology, 87(10):2626–2635, 2006.
• Strong, M. and Oakley, J.E., When Is a Model Good Enough? Deriving the Expected Value of Model Improvement via Specifying Internal Model Discrepancies, SIAM/ASA J. Uncertainty Quantif., 2(1):106–125, 2014.
• Galagali, N. and Marzouk, Y.M., Bayesian Inference of Chemical Kinetic Models from Proposed Reactions, Chem. Eng. Sci., 123:170–190, 2015.
• Málek, J. and Criado, J., Empirical Kinetic Models in Thermal Analysis, Thermochim. Acta, 203:25–30, 1992.