44 citations · 71 across the 4 of their papers we have counts for
5 papers
A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing
B. Ahmmed, M. K. Mudunuru, S. Karra +2
Accurate predictions of reactive mixing are critical for many Earth and environmental science problems. To investigate mixing dynamics over time under different scenarios, a high-f…
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…
Ensemble model aggregation using a computationally lightweight machine-learning model to forecast ocean waves
Fearghal O'Donncha, Yushan Zhang, Bei Chen +1
This study investigated an approach to improve the accuracy of computationally lightweight surrogate models by updating forecasts based on historical accuracy relative to sparse ob…
A Machine Learning Framework to Forecast Wave Conditions
Scott C. James, Yushan Zhang, Fearghal O'Donncha
A~machine learning framework is developed to estimate ocean-wave conditions. By supervised training of machine learning models on many thousands of iterations of a physics-based wa…
Core-scale solute transport model selection using Monte Carlo analysis
Bwalya Malama, Kristopher L. Kuhlman, Scott C. James
Model applicability to core-scale solute transport is evaluated using breakthrough data from column experiments conducted with conservative tracers tritium (H-3) and sodium-22, and…