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1. The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory

2. Probabilistic Regular Tree Priors for Scientific Symbolic Reasoning

3. Uncertainties and robustness with regard to the safety of a repository for high-level radioactive waste: introduction of a research initiative

4. A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms

5. Composing Partial Differential Equations with Physics-Aware Neural Networks

10. Investigating the Pilot Point Ensemble Kalman Filter for geostatistical inversion and data assimilation

11. Diagnosis of model-structural errors with a sliding time-window Bayesian analysis

12. Finite Volume Neural Network: Modeling Subsurface Contaminant Transport

14. Sequential pCN-MCMC, an efficient MCMC method for Bayesian inversion of high-dimensional multi-Gaussian priors

18. Surrogate-based Bayesian Comparison of Computationally Expensive Models: Application to Microbially Induced Calcite Precipitation

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