Peer-reviewed publications
2023. Deep parameter estimation embedded in SPDEs models: towards a stochastic formulation of neural variational scheme for learning priors and solvers. Beauchamp M., Fablet R., Johnson J.E., Benaïchouche S., Tandeo P. and Desassis N. submitted to …
2023. Ensemble-based 4DVarNet uncertainty quantification for the reconstruction of Sea Surface Height dynamics. Beauchamp M., Febvre Q., and Fablet R. Environmental Data Science2, E18. doi: 10.1017/eds.2023.
2023. Learning Neural Optimal Interpolation Models and Solvers. Beauchamp M., Febvre Q., Georgentum H., and Fablet R. In Computational Science - ICCS 2023, pp. 367-381, doi : 10.1007/978-3-031-36027-5 28
2023. 4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry. Beauchamp M., Febvre Q., Georgenthum H. and Fablet R. GMD Volume 16, Number 8, pp. 2119–2147, doi: 10.5194/gmd-16-2119-
2023. An iterative optimization scheme to accommodate inequality constraints in air quality geostatistical estimation of multivariate PM. Beauchamp M., Bessagnet B. Heliyon. Volume 9, Issue 6, doi: 10.1016/j.heliyon.2023.e
2021. Joint Interpolation and Representation Learning for Irregularly Sampled Satellite-Derived Geophysical Fields. Fablet R., Beauchamp M., Drumetz L. and Rousseau F. Frontiers in Applied Mathematics and Statistics. https://www.frontiersin.org/articles/10.3389/fams.2021.655224
2021. Data-driven spatio-temporal interpolation for satellite-derived geophysical tracers. Beauchamp M. and Fablet R. In Multitemporal Earth Observation Image Analysis
2021. Deep learning techniques applied to super-resolution chemistry transport modeling for operational uses. Bessagnet B., Beauchamp M., Menut L., Fablet R., Pisoni E. and Thunis P. Environ. Res. Commun.Volume 3, Number 8, doi: 10.1088/2515-7620/ac17f
2021. End-to-end learning of variational interpolation schemes for satellite-derived ssh data. Beauchamp M., Amar M.M., Febvre Q., Fablet R. IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 7418-7421, doi: 10.1109/IGARSS47720.2021.9554800.
2020. Intercomparison of Data-Driven and Learning-Based Interpolations of Along-Track Nadir and Wide-Swath SWOT Altimetry Observations. Beauchamp M., Fablet R., Ubelmann C., Ballarotta M. and Chapron B.Remote Sens.2020, 12, 3806. https://doi.org/10.3390/rs
2020. Data-driven and learning-based interpolations of along-track Nadir and wide-swath SWOT altimetry observations. Beauchamp M., Fablet R., Ubelmann C., Ballarotta M. and Chapron B. Proceedings of the 10th International Conference on Climate Informatics.Association for Computing Machinery, New York, NY, USA, 22-29. DOI:https://doi.org/10.1145/3429309.
2020. N-dimensional Fortran interpolation programme (NterGeo.v2020a) for geophysics sciences. Application to a back-trajectory programme (Backplumes.v2020r1) using CHIMERE or WRF outputs. Bessagnet B. and Menut L. and Beauchamp M.Geosci. Model Dev., 14, 91-106, https://doi.org/10.5194/gmd-14-91-2021
2018. A necessary distinction between spatial representativeness of an air quality monitoring station and the delimitation of exceedance area. Beauchamp M., Malherbe L., Letinois L., de Fouquet C.Environmental Monitoring and Assessment, Vol. 190, Pages 441-468, DOI: https://doi.org/10.1007/s10661-018-6788-y
2018 An additive geostatistical model for mixing total and partial PM 10 observations with CHIMERE rCTMBeauchamp M., Bessagnet B., de Fouquet C., Malherbe L., Meleux, F. Atmospheric Environment, Vol. 189, Pages 61-79, DOI: https://doi.org/10.1016/j.atmosenv.2018.06.
2018. A polynomial approximation of the traffic contributions for kriging-based interpolation of urban air quality model Beauchamp M., Malherbe L., de Fouquet C., Letinois L., Tognet, F. Environmental Modelling and Software, Vol. 105, 132-152, DOI: https://doi.org/10.1016/j.envsoft.2018.03.
2018. On numerical computation for the distribution of the convolution of N independent rectified Gaussian variables. Beauchamp M.Journal de la Soci ́et ́e Francaise de Statistique, Vol. 159, No 1. 2018
2017. Dealing with non-stationarity through explanatory variables in kriging-based air quality maps. Beauchamp M., Malherbe L., de Fouquet C.Spatial Statistics, Vol. 22 (2017) 18-46, DOI: https://doi.org/10.1016/j.spasta.2017.08.
2017. Analyzing spatio-temporal data with R: Everything you always wanted to know but were afraid to ask. Allard D. and Beauchamp M., Bel L., Desassis N. and Gabriel E., Geniaux G., Malherbe L., Martinetti D., Opitz T., Parent E., Romary T., Saby N.Journal de la Société Francaise de Statistique, Vol. 158, No 3. 2017
2015. A pragmatic approach to estimate the number of days in exceedances of limit value for PM10. Beauchamp M., Malherbe L., Fouquet C.,Atmospheric Environment, Vol. 111, 79-93, DOI: http://dx.doi.org/10.1016/j.atmosenv.2015.03.
2015. Building spatial composite indicators to analyse environmental health inequalities at a regional scale. Saib M-S., Caudeville J., Beauchamp M., Carre F., Ganry O., Trugeon, A. and Cicolella, A..Environmental Health, 2015, DOI: 10.1186/s12940-015-0054-
2015. High-resolution air quality simulation over Europe with the chemistry transport model CHIMERE. Terrenoire E., Bessagnet B., Rouil L., Tognet F., Pirovano G., Letinois L., Beauchamp M., Colette A., Thunis P., Amann M., and Menut L.Geoscientific Model Development, Vol.8, 21-42, DOI: https://doi.org/10.5194/gmd-8-21-
2014. Can further mitigation of ammonia emissions reduce exceedances of particulate matter air quality standards? Bessagnet B., Beauchamp M., Guerreiro C., de Leeuw F., Tsyrod S., Ruyssenaars R., Sauter F., Velders G., Meleux F., Colette A., Rouil L.,Environmental Science & Policy, Vol. 44, 149-163, DOI: https://doi.org/10.1016/j.envsci.2014.07.