Publications
Preprints
Cardoso, G. V., & P., M.. (2025). Predictive posterior sampling from non-stationary Gaussian process priors via Diffusion models with application to climate data. arXiv Preprint arXiv:2505. 24556.
Jansson, E., Lang, A., & P., M.. (2024). Non-stationary Gaussian random fields on hypersurfaces: Sampling and strong error analysis. arXiv Preprint arXiv:2406. 08185.
Published
Grimaud, J.-L., Desassis, N., Chourio-Camacho, D., Renard, D., P., M., Ors, F., Tissoux, H., Bessin, P., Noble, M. (2025). Kriging Alluvial Thicknesses in Valley Bottoms Using Nonstationary Geometric Anisotropies. Mathematical Geosciences, 1–21.
Zhang, Y., Yu, H., Auriol, J., & P., M. (2023). Mean-square Exponential Stabilization of Mixed-autonomy Traffic PDE System. Automatica, 170, 111859.
P., M., Kulcsár, B., Lipták, G., Kovács, M., & Szederkényi, G. (2024). The Traffic Reaction Model: A kinetic compartmental approach to road traffic modeling. Transportation research part C: emerging technologies, 158, 104435.
P., M. and Lang, A. (2023). Galerkin-Chebyshev approximation of Gaussian random fields on compact Riemannian manifolds. BIT Numerical Mathematics, 63(4), 51.
Varga, B., P., M., Kulcsár, B., Pariota, L., and Péni, T. (2023). Data-Driven Distance Metrics for Kriging-Short-Term Urban Traffic State Prediction. IEEE Transactions on Intelligent Transportation Systems.
P., M., Desassis, N., and Allard, D. (2022). Geostatistics for large datasets on Riemannian manifolds: A matrix-free approach. Journal of Data Science, 20(4):512–532.
P., M., Lang, A., & Kulcsár, B. (2022). Short-term traffic prediction using physics-aware neural networks. Transportation Research Part C: Emerging Technologies, 142, 103772.
P., M., Baykas P. B, Kulcsár B. and Lang A. (2021). Parameter and density estimation from real-world traffic data : A kinetic compartmental approach. Transportation Research Part B: Methodological 155 (2022): 210-239.
Majed, L., P., M.,, Magneron, C., Bachelot, L. L., Fagherazzi, G., Baldauf, J. J., and Raude, J. (2019). A geostatistical algorithm to better identify contextual and clinical factors associated to HPV vaccine coverage in France. Value in Health, 22, S660.
P., M. and Desassis, N. (2019). Efficient simulation of Gaussian Markov random fields by Chebyshev polynomial approximation. Spatial Statistics, 31 :100359.
Proceedings
Auriol, J., P., M., and Kulcsár, B. (2023) Mean-square exponential stabilization of coupled hyperbolic systems with random parameters. In IFAC World Congress 2023.
Desassis, N., Renard, D., P., M., and Freulon, X. (2019). Plurigaussian simulations with the Stochastic Partial Differential Equation (SPDE) approach. In Petroleum Geostatistics 2019.
P., M. and Magneron, C. (2019). SPDE geostatistical filtering for seismic data. In 81st EAGE Conference and Exhibition 2019.
P., M., Magneron, C., and Desassis, N. (2019). Geostatistical filtering of noisy seismic data using stochastic partial differential equations (SPDE). In Petroleum Geostatistics 2019.
Notes
P., M. (2023). A note on spatio-temporal random fields on meshed surfaces defined from advection-diffusion SPDEs. hal-04132148.
P., M., Desassis, N., Magneron, C., and Palmer, N. (2020). A matrix-free approach to geostatistical filtering. arXiv preprint arXiv:2004.02799.
Thesis
- P., M. (2019). Generalized random fields on Riemannian manifolds: theory and practice. Mines Paris - PSL University.