Publications

Articles

[1]M. Caillat, V. Pibernus, S. Girard, M. Ribatet, P. Armand, and C. Duchenne, “Adaptive probabilistic modeling to support decision-making in the event of accidental atmospheric releases,” Atmospheric Environment, p. 119865, 2023. https://sylvaingirard.net/pdf/caillat23-adaptive-modeling-decision.pdf.

[2]S. Girard, P. Armand, C. Duchenne, and T. Yalamas, “Stochastic perturbations and dimension reduction for modelling uncertainty of atmospheric dispersion simulations,” Atmospheric Environment, p. 117313, 2020, doi: 10.1016/j.atmosenv.2020.117313. https://sylvaingirard.net/pdf/girard20-perturbation_aam.pdf.

[3]C.-E. Gerrer and S. Girard, “Overcoming the obstacle of time-dependent model output for statistical analysis by nonlinear method,” HighTech and Innovation Journal, 2020. https://hightechjournal.org/index.php/HIJ/article/view/79/pdf.

[4]V. Mallet, A. Tilloy, D. Poulet, S. Girard, and F. Brocheton, “Meta-modeling of ADMS-urban by dimension reduction and emulation,” Atmospheric Environment, vol. 184, pp. 37–46, 2018, doi: https://doi.org/10.1016/j.atmosenv.2018.04.009.

[5]M. Kajino et al., “Lessons learned from atmospheric modeling studies after the fukushima nuclear accident: Ensemble simulations, data assimilation, elemental process modeling, and inverse modeling,” Geochemical Journal, vol. 52, no. 2, pp. 85–101, 2018, doi: https://doi.org/10.2343/geochemj.2.0503.

[6]S. Girard and T. Yalamas, “A probabilistic take on system modeling with modelica and python,” IMdR, groupe Incertitudes et Industries, 2017. https://sylvaingirard.net/pdf/girard17-probabilistic_modelica_python.pdf.

[7]S. Girard, V. Mallet, I. Korsakissok, and A. Mathieu, “Emulation and sobol’ sensitivity analysis of an atmospheric dispersion model applied to the fukushima nuclear accident,” Journal of Geophysical Research: Atmospheres, 2016, doi: 10.1002/2015JD023993. https://sylvaingirard.net/pdf/girard16-sobol_emulation_fukushima.pdf.

[8]S. Girard, I. Korsakissok, and V. Mallet, “Screening sensitivity analysis of a radionuclides atmospheric dispersion model applied to the Fukushima disaster,” Atmospheric Environment, vol. 95, no. 0, pp. 490–500, 2014, doi: 10.1016/j.atmosenv.2014.07.010. https://sylvaingirard.net/pdf/girard14-screening_fukushima.pdf.

[9]S. Girard, T. Romary, P. Stabat, J.-M. Favennec, and H. Wackernagel, “Sensitivity analysis and dimension reduction of a steam generator model for clogging diagnosis,” Reliability Engineering and System Safety, 2013. https://sylvaingirard.net/pdf/girard13-sobol_dimension_reduction_clogging.pdf.

Longer texts

[1]S. Girard, Physical and statistical models for steam generator clogging diagnosis. Springer International Publishing, 2014. https://sylvaingirard.net/pdf/girard14-physical_statistical_diagnosis.pdf.

[2]S. Girard, “Diagnostic du colmatage des générateurs de vapeur à l’aide de modèles physiques et statistiques,” PhD thesis, École des Mines ParisTech, 2012. https://pastel.archives-ouvertes.fr/pastel-00798355.

Talks & proceedings

[1]S. Girard and P. Borel, “OtFMI: Interfacing OpenTURNS with the leading standard for model exchange,” 2024. https://sylvaingirard.net/pdf/talk/girard21-modele_autoassociatif_cerfacs-ot.pdf.

[2]F. Cancelliere, S. Girard, J.-M. Bourinet, and B. Matteo, “A grey box approach for the prognostic and health management of lithium-ion batteries,” in Annual conference of the prognostics and health management (PHM) SOCIETY, 2023. https://sylvaingirard.net/pdf/talk/cancelliere23-phm.pdf.

[3]S. Girard, “La modélisation physique vue comme création de connaissance : Un moteur de l’innovation,” in REX, capitalisation des connaissances, et modélisation, 2022. https://sylvaingirard.net/pdf/talk/girard22-modele_moteur_innovation.pdf.

[4]S. Girard, “The living digital twin: 3 factors to get beyond the hype,” in MATHIAS Days 2022, 2022. https://sylvaingirard.net/pdf/talk/girard22-mathias22.pdf.

[5]S. Girard, H. Blervaque, and Y. Souami, “Industrialiser le jumeau numérique – application à la maintenance prévisionnelle d’un compresseur d’air,” in GST mécanique et incertain, 2022. https://sylvaingirard.net/pdf/talk/girard22-jumeau_gst_meca_incertain.pdf.

[6]F. Cancelliere and S. Girard, “Management of a free-floating electrical scooters fleet,” in Congrès :math:`lambda mu` 23, 2022. https://hal.science/hal-03966655.

[7]L. Xavier, M. Chetra, T. Ahmadali, and S. Girard, “Geometrical imperfections in lattice structures: A simulation strategy to predict strength variability,” in 14th WCCM-ECCOMAS congress, 2021, doi: 10.23967/wccm-eccomas.2020.155. https://www.scipedia.com/wd/images/4/43/Draft_Content_164761637p5255.pdf.

[8]R. Périllat, S. Girard, and I. Korsakissok, “Outils rapides pour l’intégration des incertitudes dans la gestion de crise nucléaire,” 2021. https://www.imdr.eu/offres/gestion/events_818_51780_non-2229/les-rencontres-intergtr-la-notion-d-integration-dans-les-approches-systemiques-pour-apprehender-la-complexite.html.

[9]R. Périllat, S. Girard, I. Korsakissok, and E. Quentric, “Emulators for the rapid prediction of consequences in case of nuclear hazards.” Poster at HARMO20, 2021.

[10]C.-E. Gerrer, H. Blervaque, J. Schueller, D. Bouskela, and S. Girard, “Analysis and reduction of models using persalys,” in Modelica 2021 (industrial user track), 2021. https://sylvaingirard.net/pdf/talk/gerrer-modelica21.pdf.

[11]G. Blondet, S. Girard, J.-M. Heurtier, and T. Yalamas, “A new methodology for fast lifespan prediction of offshore structures,” in ESREL 2021, 31st european safety and reliability conference, 2021. https://sylvaingirard.net/pdf/talk/blondet-esrel21.pdf.

[12]S. Girard, “Réduction de dimension par des modèles auto-associatifs,” in Atelier OpenTURNS–CERFACS : Méta-modélisation en grande dimension, 2021. https://sylvaingirard.net/pdf/talk/girard21-modele_autoassociatif_cerfacs-ot.pdf.

[13]C.-E. Gerrer and S. Girard, “Health monitoring using statistical learning and digital twins,” in NAFEMS20, 2020. https://sylvaingirard.net/pdf/gerrer20-health_monitoring_digital_twins.pdf.

[14]R. Périllat, S. Girard, and I. Korsakissok, “Solutions rapides pour la prévision des risques de pollution atmosphérique,” in Lambda mu 22, 2020. https://sylvaingirard.net/pdf/talk/perillat20-lambdamu21.pdf.

[15]S. Girard, P. Armand, C. Duchenne, and T. Yalamas, “Generalized perturbation scheme for uncertainty propagation in atmospheric dispersion simulations,” in 19th international conference on harmonisation within atmospheric dispersion modelling for regulatory purposes, HARMO19, 2019. https://sylvaingirard.net/pdf/girard19-harmo19.pdf.

[16]C.-E. Gerrer and S. Girard, “Non linear dimension reduction of dynamic model output,” in Proceedings of the 13th international modelica conference, regensburg, germany, march 4–6, 2019, 2019, doi: 10.3384/ecp19157189. https://sylvaingirard.net/pdf/gerrer19-aam_modelica.pdf.

[17]C.-E. Gerrer and S. Girard, “Health monitoring by physical modeling and statistical learning,” in 4th international conference on system reliability and safety, 2019.

[18]S. Girard and T. Yalamas, “Health monitoring by statistical learning and physical modelling,” in Computational science engineering, data science & artificial intelligence (MATHIAS 2019), 2019. https://sylvaingirard.net/pdf/talk/girard19-mathias.pdf.

[19]S. Girard, “Expériences numériques avec des modèles spatio-temporels,” in Rencontre chercheurs–ingénieurs « appréhender la grande dimension », 2019.

[20]S. Girard, T. Yalamas, and M. Baudin, “Statistical learning and 0D/1D modelling: Application to battery ageing,” in Lambda mu 21 proceedings, 2018. https://sylvaingirard.net/pdf/girard18-battery_ageing.pdf.

[21]S. Girard, “Pronostic de durée de vie en fatigue par apprentissage statistique et modélisation physique,” in Journées de la conception robuste et fiable, 2017. https://sylvaingirard.net/pdf/talk/girard17-pronostic_fatigue.pdf.

[22]C. Duchenne, P. Armand, M. Marcilhac, S. Girard, and T. Yalamas, “A new method for assessing the uncertainty associated with 3D dispersion simulations in any variable meteorological conditions,” in 18th international conference on harmonisation within atmospheric dispersion modelling for regulatory purposes, HARMO18, 2017.

[23]S. Girard, I. Korsakissok, and V. Mallet, “Sensitivity analysis of radionuclides atmospheric dispersion following the Fukushima accident,” in European geosciences union (EGU) general assembly, 2014.

[24]S. Girard, T. Romary, P. Stabat, J.-M. Favennec, and H. Wackernagel, “Towards a better understanding of clogged steam generators: A sensitivity analysis of dynamic thermohydraulic model output,” in 19th international conference on nuclear engineering (ICONE19), 2011. https://sylvaingirard.net/pdf/girard11-sensitivity_dynamic_clogging.pdf.

[25]S. Girard, T. Romary, and H. Wackernagel, “Réduction de dimension d’un modèle thermohydraulique,” in Xèmes journées de géostatistique, 2011.