Bio

I am a staff member at Sandia National Laboratories in Livermore, California. I joined the lab in June 2020 as a postdoctoral appointee working with Habib Najm. I obtained my PhD in Computer Science at KU Leuven, supervised by prof. Stefan Vandewalle and prof. Dirk Nuyens.

My primary research interests lie in the field of uncertainty quantification and statistical learning, where I focus on the development and application of advanced methodologies to enhance the predictive capabilities of large-scale physical and computational models.

Publications

* indicates equal contribution

1. Robbe, P. GaussianRandomFields.jl: A Julia package to generate and sample from Gaussian random fields. Journal of Open Source Software 8, 89 (2023), 5595 Article Cite
2. Tran, A., Robbe, P., and Lim, H. Multi-faceted Uncertainty Quantification for Structure-Property Relationship with Crystal Plasticity Finite Element. In Proceedings of the TMS Annual Meeting & Exhibition (2023). The Minerals, Metals & Materials Series, pp. 596-606 Article Preprint Cite
3. Tran, A.*, Robbe, P.*, and Lim, H. Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods. Materialia 27, 101705 (2023), pp. 1-38 Article Preprint Cite
4. Robbe, P., Andersson, A., Bonnet, L., Casey, T., Cooper, M. W. D., Matthews, C., Sargsyan, K., Najm, H. N. Bayesian calibration with summary statistics for the prediction of xenon diffusion in UO₂ nuclear fuel. Computational Materials Science 225, 112184 (2023), pp. 1-38 Article Preprint Cite
5. Robbe, P., Blondel, S., Casey, T. A., Lasa, A., Sargsyan, K., Wirth, B. D. and Najm, H. N. Global sensitivity analysis of a coupled multiphysics model to predict surface evolution in fusion plasma-surface interactions. Computational Materials Science 226, 112229 (2023), pp. 1-24 Article Preprint Cite
6. Mortier, B.*, Robbe, P.*, Baelmans, M., and Samaey, G. Multilevel asymptotic-preserving Monte Carlo for kinetic-diffusive particle simulations of the Boltzmann-BGK equation. Journal of Computational Physics, 450, 110736 (2022), pp. 1-36 Article Cite
7. Blondeel, P., Robbe, P., François, S., Lombaert, G., and Vandewalle, S. An overview of p-refined multilevel quasi-Monte Carlo applied to the geotechnical slope stability problem. In Proceedings of the 6th ECCOMAS Young Investigators Conference (ECCOMAS-YIC) (2021), Editorial Universitat Politècnica de València, pp. 25-35. Article Cite
8. Blondeel, P., Robbe, P., François, S., Lombaert, G., and Vandewalle, S. Improving the rate of convergence of the quasi-Monte Carlo method in estimating expectations on a geotechnical slope stability problem. In Proceedings of the 4th ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Sciences and Engineering (ECCOMAS) (2021) Article Cite
9. Blondeel, P., Robbe, P., François, S., Lombaert, G., and Vandewalle, S. A p-refined multilevel quasi-Monte Carlo method with supermesh construction applied to a geotechnical slope stability problem. In Proceedings of the 13th International Conference on Structural Safety and Reliability (ICOSSAR) (2021) Article Cite
10. Blondeel, P., Robbe, P., François, S., Lombaert, G., and Vandewalle, S. Benchmarking the p-MLQMC method on a geotechnical engineering problem. In Proceedings of the 14th World Congress on Computational Mechanics and ECCOMAS Congress (2021) Article Cite
11. Blondeel, P., Robbe, P., Van hoorickx, C., François, S., Lombaert, G., and Vandewalle, S. p-Refined Multilevel Quasi-Monte Carlo for Galerkin Finite Element Methods with Applications in Civil Engineering. Algorithms 13, 5 (2020), pp. 1-30 Article Cite
12. Robbe, P., Nuyens, D., and Vandewalle, S. Enhanced Multi-Index Monte Carlo by means of Multiple Semi-Coarsened Multigrid for Anisotropic Diffusion Problems. Numerical Linear Algebra with Applications 28, 3 (2020), e2281 Article Preprint Cite
13. Blondeel, P., Robbe, P., François, S., Lombaert, G., and Vandewalle, S. On the selection of random field evaluation points in the p-MLQMC method. In Monte Carlo and Quasi-Monte Carlo Methods (2020), Keller, A., eds., Springer Proceedings in Mathematics & Statistics, vol. 387, pp. 185-203 Article Preprint Cite
14. Robbe, P. Multilevel Uncertainty Quantification Methods for Robust Design of Industrial Applications. Vandewalle, S., and Nuyens, D., supervisors. PhD thesis, KU Leuven (2019) Article Cite
15. Robbe, P., Nuyens, D., and Vandewalle, S. Recycling Samples in the Multigrid Multilevel Quasi-Monte Carlo Method. SIAM Journal on Scientific Computing 41, 5 (2019), S37-S60 Article Preprint Cite
16. Blondeel, P., Robbe, P., Vanhoorickx, C., Lombaert, G., and Vandewalle, S. Multilevel Sampling with Monte Carlo and Quasi-Monte Carlo Methods for Uncertainty Quantification in Structural Engineering. In Proceedings of the 13th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP13) (2019) Article Cite
17. Blondeel, P., Robbe, P., Van hoorickx, C., Lombaert, G., and Vandewalle, S. Multilevel Monte Carlo Applied to a Structural Engineering Model with Random Material Parameters. In Proceedings of the 28th edition of the Biennial ISMA conference on Noise and Vibration Engineering (ISMA) (2018), Desmet, W., Pluymers, B., Moens, D., and Rottiers, W., eds., pp. 4899-4913 Article Preprint Cite
18. Robbe, P., Nuyens, D., and Vandewalle, S. A Dimension-Adaptive Multi-Index Monte Carlo Method Applied to a Model of a Heat Exchanger. In Monte Carlo and Quasi-Monte Carlo Methods (2018), Owen, A. B., and Glynn, P., eds., Springer Proceedings in Mathematics & Statistics, vol. 241, pp. 429-445 Article Preprint Cite
19. Robbe, P., Nuyens, D., and Vandewalle, S. A Multi-Index Quasi-Monte Carlo Algorithm for Lognormal Diffusion Problems. SIAM Journal on Scientific Computing 39, 5 (2017), S851-S872 Article Preprint Cite

Software

GaussianRandomFields

This is a Julia package used to generate and sample from Gaussian random fields.

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Julia

MultilevelEstimators

This is a Julia package that implements the multilevel sampling methods developed during my PhD.

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Julia

Dakota

I've added QMC sampling methods to Dakota, Sandia's flagship UQ software package.

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C++

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