10 citations · 11 across the 4 of their papers we have counts for
4 papers
Quantifying local and global mass balance errors in physics-informed neural networks
Md Lal Mamud, Maruti K. Mudunuru, Satish Karra +1
Physics-informed neural networks (PINN) have recently become attractive for solving partial differential equations (PDEs) that describe physics laws. By including PDE-based loss fu…
GeoThermalCloud: Machine Learning for Geothermal Resource Exploration
Maruti K. Mudunuru, Velimir V. Vesselinov, Bulbul Ahmmed
This paper presents a novel ML-based methodology for geothermal exploration towards PFA applications. Our methodology is provided through our open-source ML framework, GeoThermalCl…
AdjointNet: Constraining machine learning models with physics-based codes
Satish Karra, Bulbul Ahmmed, Maruti K. Mudunuru
Physics-informed Machine Learning has recently become attractive for learning physical parameters and features from simulation and observation data. However, most existing methods…
PFLOTRAN-SIP: A PFLOTRAN Module for Simulating Spectral-Induced Polarization of Electrical Impedance Data
B. Ahmmed, M. K. Mudunuru, S. Karra +3
Spectral induced polarization (SIP) is a non-intrusive geophysical method that is widely used to detect sulfide minerals, clay minerals, metallic objects, municipal wastes, hydroca…