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Enhancing model predictability for a scramjet using probabilistic learning on manifoldsAIAA Journal, 2019, 57 (1), pp.365-378. ⟨10.2514/1.J057069⟩
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Polynomial chaos representation of a stochastic preconditionerInternational Journal for Numerical Methods in Engineering, 2005, 64 (5), pp.618-634. ⟨10.1002/nme.1382⟩
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istex
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Identification of chaos representations of elastic properties of random media using experimental vibration testsComputational Mechanics, 2007, 39 (6), pp.831-838. ⟨10.1007/s00466-006-0072-7⟩
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hal-00686150v1
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Remarks on stochastic properties of materials through finite deformationsInternational Journal for Multiscale Computational Engineering, 2015, 13 (4), pp.367-374. ⟨10.1615/IntJMultCompEng.2015013959⟩
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hal-01162152v1
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Probabilistic Learning on Manifolds (PLoM)Machine Learning in Science and Engineering (MISE 2020), Columbia University, Dec 2020, New York, United States
Conference papers
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Probabilistic machine learning with intrinsic constraintsSIAM Conference on Mathematics of Data Science (MSD20), May 2020, Cincinnati, Ohio, United States
Conference papers
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Maximum likelihood estimation of stochastic chaos representations from experimental dataInternational Journal for Numerical Methods in Engineering, 2006, 66 (6), pp.978-1001. ⟨10.1002/nme.1576⟩
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hal-00686154v1
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Inverse problem for the identification of Chaos representations of random fields using experimental vibrational testsInternational Conference on Noise and Vibration Engineering (ISMA2006), Katholieke Universiteit Leuven, Sep 2006, Leuven, Belgium. pp.4117-4123
Conference papers
hal-00686204v1
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A probabilistic model for bounded elasticity tensor random fields with application to polycrystalline microstructuresComputer Methods in Applied Mechanics and Engineering, 2011, 200 (17-20), pp.1637-1648. ⟨10.1016/j.cma.2011.01.016⟩
Journal articles
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Identification of Bayesian posteriors for coefficients of chaos expansionsJournal of Computational Physics, 2010, 229 (9), pp.3134-3154. ⟨10.1016/j.jcp.2009.12.033⟩
Journal articles
hal-00684317v1
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Probabilistic models and sampling on manifoldsSIAM UQ, Apr 2018, Garden Grove, United States
Conference papers
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Stochastic reduced-order model for spent nuclear fuel containersEURODYN 2020, XI International Conference on Structural Dynamics, Nov 2020, Athènes (virtual), Greece
Conference papers
hal-03245567v1
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Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small datasetStatistics and Computing, 2020, 30 (5), pp.1433-1457. ⟨10.1007/s11222-020-09954-6⟩
Journal articles
hal-02640469v1
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Data-driven probabilistic learning on manifoldsThe 4th conference on Model Reduction of Parametrized Systems (MoRePaS IV), Apr 2018, Nantes, France
Conference papers
hal-01810457v1
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Stochastic representation for anisotropic permeability tensor random fieldsInternational Journal for Numerical and Analytical Methods in Geomechanics, 2012, 36 (13), pp.1592-1608. ⟨10.1002/nag.1081⟩
Journal articles
istex
hal-00724651v1
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Probabilistic learning on manifolds (PLoM) with partitionInternational Journal for Numerical Methods in Engineering, 2022, 123 (1), pp.268-290. ⟨10.1002/nme.6856⟩
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hal-03381363v1
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Probabilistic learning on manifold for optimization under uncertainties(Plenary Lecture), UNCECOMP 2017, 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering and COMPDYN 2017, 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Jun 2017, Rhodes Island, Greece. pp.1-15
Conference papers
hal-01541216v1
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Machine Learning for Efficient Sampling in UQThe 13th World Congress of Computational Mechanics (WCCM 2018) and Second Pan American Congress on Computational Mechanics (PANACM II), Jul 2018, New York, United States
Conference papers
hal-01876749v1
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Manifold sampling for data-driven UQ and optimization (Keynote lecture presented by R. Ghanem)USNCCM 2017, 14th U. S. National Congress on Computational Mechanics, Jul 2017, Montreal, Canada
Conference papers
hal-01566425v1
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UQ Challenge Benchmarks OverviewSIAM Conference on Uncertainty Quantification, SIAM, Mar 2014, Savannah, Georgia, United States. pp.Pages: 1-1
Conference papers
hal-00989658v1
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Probabilistic learning on manifolds for prognosis and characterization of the digital twinMechanistic Machine Learning and Digital Twins for Computational Science, Engineering and Technology (MMLDT-CSET 2021), Sep 2021, San Diego, CA, United States
Conference papers
hal-03381365v1
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Probabilistic learning for efficient optimization under risk constraints2018 SIAM Annual Meeting, SIAM, Jul 2018, Portland, Oregon, United States
Conference papers
hal-01876003v1
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Reduced chaos decomposition with random coefficients of vector-valued random variables and random fieldsComputer Methods in Applied Mechanics and Engineering, 2009, 98 (21-26), pp.1926-1934. ⟨10.1016/j.cma.2008.12.035⟩
Journal articles
hal-00684487v1
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Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasetsComputer Methods in Applied Mechanics and Engineering, 2021, 380, pp.113777. ⟨10.1016/j.cma.2021.113777⟩
Journal articles
hal-03179788v1
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Data-driven sampling and prediction on manifoldsUSACM 2017, Thematic Workshop on Uncertainty Quantification and Data-Driven Modeling,, USACM, Mar 2017, Austin, Texas, United States
Conference papers
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Data-driven probability concentration and sampling on manifoldJournal of Computational Physics, 2016, 321, pp.242-258. ⟨10.1016/j.jcp.2016.05.044⟩
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Updating an uncertain and expensive computational model in structural dynamics based on one single target FRF using a probabilistic learning toolComputational Mechanics, 2023, 71, pp.1161-1177. ⟨10.1007/s00466-023-02301-2⟩
Journal articles
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Hybrid sampling/spectral method for solving stochastic coupled problemsSIAM/ASA Journal on Uncertainty Quantification, 2013, 1 (1), pp.218-243. ⟨10.1137/120894403⟩
Journal articles
hal-00829060v1
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A probabilistic learning on manifolds as a new statistical tool in data science with applications in computational mechanicsInternational Workshop on Data Science in Civil Engineering, Jun 2019, Shanghai, China
Conference papers
hal-02175618v1
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Probabilistic machine learning for the small-data challenge in computational science2019, pp.3-9
Other publications
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