research
∙
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...
share
research
∙
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...
research
∙
11/23/2021
Composing Partial Differential Equations with Physics-Aware Neural Networks
We introduce a compositional physics-aware neural network (FINN) for lea...
research
∙
06/25/2021
Uncertainty-aware Validation Benchmarks for Coupling Free Flow and Porous-Medium Flow
A correct choice of interface conditions and useful model parameters for...
research
∙
04/13/2021
Finite Volume Neural Network: Modeling Subsurface Contaminant Transport
Data-driven modeling of spatiotemporal physical processes with general d...
research
∙
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...
research
∙
02/08/2018