References
- 1
Meng Yang Gu, Jesus Palomo, and James O. Berger. Robustgasp: robust Gaussian stochastic process emulation in R. The R Journal, 11(1):112–136, 2019. doi:10.32614/RJ-2019-011.
- 2
Meng Yang Gu and James O. Berger. Parallel partial Gaussian process emulation for computer models with massive output. Annals of Applied Statistics, 10(3):1317–1347, 2016. doi:10.1214/16-AOAS934.
- 3
H. Zhao, F. Amann, and J. Kowalski. Emulator-based global sensitivity analysis for flow-like landslide run-out models. Landslides, ():, 2021. doi:https://doi.org/10.1007/s10346-021-01690-w.
- 4
Hu Zhao. Gaussian processes for sensitivity analysis, Bayesian inference, and uncertainty quantification in landslide research. PhD thesis, RWTH Aachen University, 2021. doi:10.18154/RWTH-2021-11693.
- 5
Meng Yang Gu, Xiao Jing Wang, and James O. Berger. Robust Gaussian stochastic process emulation. Annals of Statistics, 46(6A):3038–3066, 2018. doi:10.1214/17-AOS1648.
- 6
Hong Qiao Wang and Jing Lai Li. Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions. Neural Computation, 30(11):3072–3094, 2018. doi:10.1162/neco_a_01127.
- 7
Kirthevasan Kandasamy, Jeff Schneider, and Barnabás Póczos. Query efficient posterior estimation in scientific experiments via Bayesian active learning. Artificial Intelligence, 243:45–56, 2017. doi:https://doi.org/10.1016/j.artint.2016.11.002.
- 8
Hu Zhao and Julia Kowalski. Bayesian active learning for parameter calibration of landslide run-out models. Landslides, 19():2033–2045, 2022. doi:https://doi.org/10.1007/s10346-022-01857-z.
- 9
Andrea Saltelli. Making best use of model evaluations to compute sensitivity indices. Computer Physics Communications, 145(2):280–297, 2002. doi:https://doi.org/10.1016/S0010-4655(02)00280-1.
- 10
Andrea Saltelli, Paola Annoni, Ivano Azzini, Francesca Campolongo, Marco Ratto, and Stefano Tarantola. Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Computer Physics Communications, 181(2):259–270, 2010. doi:https://doi.org/10.1016/j.cpc.2009.09.018.
- 11
Jon Herman and Will Usher. SALib: an open-source Python library for sensitivity analysis. The Journal of Open Source Software, 2017. doi:10.21105/joss.00097.
- 12
Jan-Thomas Fischer, Julia Kowalski, and Shiva P. Pudasaini. Topographic curvature effects in applied avalanche modeling. Cold Regions Science and Technology, 74-75:21–30, 2012. doi:https://doi.org/10.1016/j.coldregions.2012.01.005.
- 13
M. Mergili, J. T. Fischer, J. Krenn, and S. P. Pudasaini. R.avaflow v1, an advanced open-source computational framework for the propagation and interaction of two-phase mass flows. Geoscientific Model Development, 10(2):553–569, 2017. doi:10.5194/gmd-10-553-2017.
- 14
M. Christen, J. Kowalski, and P. Bartelt. RAMMS: numerical simulation of dense snow avalanches in three-dimensional terrain. Cold Regions Science and Technology, 63(1):1–14, 2010. doi:https://doi.org/10.1016/j.coldregions.2010.04.005.
- 15
Loic Le Gratiet, Claire Cannamela, and Bertrand Iooss. A Bayesian approach for global sensitivity analysis of (multifidelity) computer codes. SIAM/ASA Journal on Uncertainty Quantification, 2(1):336–363, 2014. doi:10.1137/130926869.