We present a stochastic deep collocation method (DCM) based on neural architecture
search (NAS) and transfer learning for heterogeneous porous media. We first carry out a …

The present work documents the current state of development for our MATLAB/GNU Octave-
based open source toolbox FESTUNG (Finite Element Simulation Toolbox for UNstructured …

We present an approach for quantifying displacement fronts in heterogeneous porous media
based on the concept of time-dependent apparent dispersion coefficients. The concept of …

In this work, a modified neural architecture search method (NAS) based physics-informed
deep learning model is presented for stochastic analysis in heterogeneous porous material …

In this work, a modified neural architecture search method (NAS) based physics-informed
deep learning model is presented to solve the groundwater flow problems in porous media …

The effective conductivity of heterogeneous aquifers K efu relates the mean flux to the mean
head gradient in mean uniform (natural gradient) flow. While K efu depends on the hydraulic …

In this study, we developed a novel boundary-type meshless approach for dealing with two-
dimensional transient flows in heterogeneous layered porous media. The novelty of the …

In this article, we present new random walk methods to solve flow and transport problems in
saturated/unsaturated porous media, including coupled flow and transport processes in …

Simulating groundwater flows in heterogeneous aquifers is one of the most widely studied
problems. The heterogeneity is modeled through random hydraulic conductivity fields log …

Flow and multicomponent reactive transport in saturated/unsaturated porous media are
modeled by ensembles of computational particles moving on regular lattices according to …