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06/26/2023
The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory
Artificial Intelligence and Machine learning have been widely used in va...
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06/14/2023
Probabilistic Regular Tree Priors for Scientific Symbolic Reasoning
Symbolic Regression (SR) allows for the discovery of scientific equation...
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04/12/2022
The sparse Polynomial Chaos expansion: a fully Bayesian approach with joint priors on the coefficients and global selection of terms
Polynomial chaos expansion (PCE) is a versatile tool widely used in unce...
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11/23/2021
Composing Partial Differential Equations with Physics-Aware Neural Networks
We introduce a compositional physics-aware neural network (FINN) for lea...
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08/04/2021
Investigating the Pilot Point Ensemble Kalman Filter for geostatistical inversion and data assimilation
Parameter estimation has a high importance in the geosciences. The ensem...
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07/20/2021
Diagnosis of model-structural errors with a sliding time-window Bayesian analysis
Deterministic hydrological models with uncertain, but inferred-to-be-tim...
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04/13/2021
Finite Volume Neural Network: Modeling Subsurface Contaminant Transport
Data-driven modeling of spatiotemporal physical processes with general d...
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03/24/2021
Sequential pCN-MCMC, an efficient MCMC method for Bayesian inversion of high-dimensional multi-Gaussian priors
In geostatistics, Gaussian random fields are often used to model heterog...
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11/25/2020
Surrogate-based Bayesian Comparison of Computationally Expensive Models: Application to Microbially Induced Calcite Precipitation
Geochemical processes in subsurface reservoirs affected by microbial act...
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11/23/2019
Using Surrogate Models and Data Assimilation for Efficient Mobile Simulations
Numerical simulations on mobile devices are an important tool for engine...
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02/08/2018