activity
20172026
most citedFifth order finite volume WENO in general orthogonally-curvilinear coordinates

9 citations · 20 across the 7 of their papers we have counts for

collaborators

7 papers

physics.geo-ph2026

HydroFirn: A numerical model for large-scale multidimensional firn hydrology

Mohammad Afzal Shadab, Surendra Adhikari, C. Max Stevens +4

Observations show the multidimensional dynamics of meltwater and distribution of ice layers in the firn on the Greenland Ice Sheet. However, state-of-the-art large-scale models for…

physics.flu-dyn2025

A vertically integrated model with phase change for aquifers in cold firn

Mohammad Afzal Shadab, Howard A. Stone, Reed M. Maxwell

Surface meltwater from glaciers and ice sheets contributes significantly to sea-level rise, yet the processes governing its transport and retention within cold firn remain poorly c…

astro-ph.EP2024

History and Habitability of the LP 890-9 Planetary System

Rory Barnes, Laura N. R. do Amaral, Jessica Birky +42

We present numerous aspects of the evolution of the LP 890-9 (SPECULOOS-2/TOI-4306) planetary system, focusing on the likelihood that planet c can support life. We find that the ho…

physics.flu-dyn2024★ 2 cited

A unified kinematic wave theory for melt infiltration into firn

Mohammad Afzal Shadab, Anja Rutishauser, Cyril Grima +1

Motivated by the refreezing of melt water in firn we revisit the one-dimensional percolation of liquid water and non-reactive gas in porous ice. We analyze the dynamics of infiltra…

physics.comp-ph2022★ 3 cited

A hyperbolic-elliptic PDE model and conservative numerical method for gravity-dominated variably-saturated groundwater flow

Mohammad Afzal Shadab, Marc Andre Hesse

Richards equation is often used to represent two-phase fluid flow in an unsaturated porous medium when one phase is much heavier and more viscous than the other. However, it cannot…

physics.geo-ph2021★ 6 cited

Investigating Steady Unconfined Groundwater Flow using Physics Informed Neural Networks

Mohammad Afzal Shadab, DingCheng Luo, Yiran Shen +2

A novel deep learning technique called Physics Informed Neural Networks (PINNs) is adapted to study steady groundwater flow in unconfined aquifers. This technique utilizes informat…

physics.comp-ph2017★ 9 cited

Fifth order finite volume WENO in general orthogonally-curvilinear coordinates

Mohammad Afzal Shadab, Dinshaw Balsara, Wei Shyy +1

High order reconstruction in the finite volume (FV) approach is achieved by a more fundamental form of the fifth order WENO reconstruction in the framework of orthogonally-curvilin…