88 citations · 112 across the 6 of their papers we have counts for
6 papers
GeoMCP: A Trustworthy Framework for AI-Assisted Analytical Geotechnical Engineering
Yared W. Bekele
Analytical methods underpin geotechnical engineering practice, yet their implementation remains fragmented across error-prone spreadsheets and opaque proprietary software. While La…
GeoSim.AI: AI assistants for numerical simulations in geomechanics
Yared W. Bekele
The ability to accomplish tasks via natural language instructions is one of the most efficient forms of interaction between humans and technology. This efficiency has been translat…
Physics-informed neural networks with curriculum training for poroelastic flow and deformation processes
Yared W. Bekele
Physics-Informed Neural Networks (PINNs) have emerged as a highly active research topic across multiple disciplines in science and engineering, including computational geomechanics…
Physics-informed deep learning for flow and deformation in poroelastic media
Yared W. Bekele
A physics-informed neural network is presented for poroelastic problems with coupled flow and deformation processes. The governing equilibrium and mass balance equations are discus…
Deep Learning for One-dimensional Consolidation
Yared W. Bekele
Neural networks with physical governing equations as constraints have recently created a new trend in machine learning research. In line with such efforts, a deep learning model fo…
On Mixed Isogeometric Analysis of Poroelasticity
Yared W. Bekele, Eivind Fonn, Trond Kvamsdal +2
Pressure oscillations at small time steps have been known to be an issue in poroelasticity simulations. A review of proposed approaches to overcome this problem is presented. Criti…