Comprehensive uncertainty analysis for surface water and groundwater projections under climate change based on a lumped geo-hydrological model. Journal of hydrology, (626)B:130323, Elsevier, 2023. [PUMA: mult ubs_10002 ubs_10021 ubs_20002 ubs_20019 ubs_30028 ubs_30165 ubs_40041 unibibliografie wos]
The method of forced probabilities : a computation trick for Bayesian model evidence. Computational geosciences, (27)1:45-62, Springer, 2023. [PUMA: mult oa ubs_10002 ubs_10021 ubs_20002 ubs_20019 ubs_30028 ubs_30165 ubs_40041 unibibliografie wos]
Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator : Data and Software. 2023. [PUMA: darus mult ubs_10002 ubs_10021 ubs_20002 ubs_20019 ubs_30028 ubs_30165 ubs_40041 unibibliografie]
Diagnosing similarities in probabilistic multi-model ensembles : an application to soil–plant-growth-modeling. Modeling earth systems and environment, (8)4:5143-5175, Springer, 2022. [PUMA: mult oa ubs_10002 ubs_10021 ubs_20002 ubs_20019 ubs_30028 ubs_30165 ubs_40041 unibibliografie wos]
Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network. 2022. [PUMA: darus mult ubs_10002 ubs_10021 ubs_20002 ubs_20019 ubs_30028 ubs_30165 ubs_40041 unibibliografie]
Diagnosis of Model Errors With a Sliding Time-Window Bayesian Analysis. Water resources research, (58)2:e2021WR030590, Wiley, 2022. [PUMA: mult oa ubs_10002 ubs_10021 ubs_20002 ubs_20019 ubs_30028 ubs_30165 ubs_40041 unibibliografie wos]
PDEBench Datasets : Data for "PDEBench: An Extensive Benchmark for Scientific Machine Learning". 2022. [PUMA: darus mult ubs_10002 ubs_10005 ubs_10021 ubs_20002 ubs_20008 ubs_20019 ubs_30028 ubs_30082 ubs_30165 ubs_40041 ubs_40349 ubs_40434 unibibliografie]
PDEBench Pretrained Models : Pretrained models for "PDEBench: An Extensive Benchmark for Scientific Machine Learning". 2022. [PUMA: darus mult ubs_10002 ubs_10005 ubs_10021 ubs_20002 ubs_20008 ubs_20019 ubs_30028 ubs_30082 ubs_30165 ubs_40041 ubs_40349 ubs_40434 unibibliografie]
Replication Data for: Overcoming the model-data-fit problem in porous media: A quantitative method to compare invasion-percolation models to high-resolution data : modeling data and Post-processing codes for the manuscript. 2021. [PUMA: darus mult ubs_10002 ubs_10021 ubs_20002 ubs_20019 ubs_30028 ubs_30165 ubs_40041 unibibliografie]
A unified framework for quantitative interdisciplinary flood risk assessment. American Geophysical Union, Fall Meeting 2020, H177-01, Smithsonian Astrophysical Observatory, 2020. [PUMA: liste mult ubs_10002 ubs_10021 ubs_20002 ubs_20019 ubs_30028 ubs_30165 ubs_40040 ubs_40041 unibibliografie] URL
Bayesian model evidence as a model evaluation metric. 19th EGU General Assembly, EGU2017, 13390, Smithsonian Astrophysical Observatory, 2017. [PUMA: liste mult ubs_10002 ubs_10012 ubs_20002 ubs_20019 ubs_30028 ubs_30165 ubs_40041 unibibliografie] URL
Overcoming the Model-to-Experimental Data Fit Problem in Porous Media : a New Quantitative Method to Evaluate and Compare Models. American Geophysical Union, Fall Meeting 2020, H009-0020, Smithsonian Astrophysical Observatory, 2020. [PUMA: liste mult ubs_10002 ubs_10021 ubs_20002 ubs_20019 ubs_30028 ubs_30165 ubs_40041 unibibliografie] URL
Diagnosing model-structural errors with a sliding time window Bayesian analysis. 22nd EGU General Assembly, 2991, Smithsonian Astrophysical Observatory, 2020. [PUMA: liste mult ubs_10002 ubs_10021 ubs_20002 ubs_20019 ubs_30028 ubs_30165 ubs_40041 unibibliografie] URL
Providing relevant uncertainty information to decision makers : Subjective post-processing of rigorous Bayesian uncertainty assessment of model projections. American Geophysical Union, Fall Meeting 2020, GC073-0011, Smithsonian Astrophysical Observatory, 2020. [PUMA: liste mult ubs_10002 ubs_10021 ubs_20002 ubs_20019 ubs_30028 ubs_30165 ubs_40041 unibibliografie] URL
Bayesian model selection under computational time constraints : application to river modeling. American Geophysical Union, Fall Meeting 2018, H51O-1495, Smithsonian Astrophysical Observatory, 2018. [PUMA: liste mult ubs_10002 ubs_10012 ubs_20002 ubs_20019 ubs_30028 ubs_30165 ubs_40041 unibibliografie] URL
Regime-and-memory model (RMM) Code. 2021. [PUMA: darus mult ubs_10002 ubs_10021 ubs_20002 ubs_20019 ubs_30028 ubs_30165 ubs_40041 unibibliografie]
Sampling Strategies of the Regime-and-memory model (RMM). 2021. [PUMA: darus mult ubs_10002 ubs_10021 ubs_20002 ubs_20019 ubs_30028 ubs_30165 ubs_40041 unibibliografie]
Code for relative permeabilities for two-phase flow between parallel plates with slip conditions. 2021. [PUMA: darus mult ubs_10002 ubs_10021 ubs_20002 ubs_20019 ubs_30028 ubs_30165 ubs_40040 ubs_40041 unibibliografie]
Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic System. 2020. [PUMA: darus mult ubs_10002 ubs_10021 ubs_20002 ubs_20019 ubs_30028 ubs_30165 ubs_40041 unibibliografie]
Trained ANN Parameters for Physics-inspired Artificial Neural Network for Dynamic System. 2020. [PUMA: darus mult ubs_10002 ubs_10021 ubs_20002 ubs_20019 ubs_30028 ubs_30165 ubs_40041 unibibliografie]