6 citations · 7 across the 3 of their papers we have counts for
5 papers
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…
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…
Learning to regularize with a variational autoencoder for hydrologic inverse analysis
Daniel O'Malley, John K. Golden, Velimir V. Vesselinov
Inverse problems often involve matching observational data using a physical model that takes a large number of parameters as input. These problems tend to be under-constrained and…
Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clustering
Valentin Stanev, Velimir V. Vesselinov, A. Gilad Kusne +3
Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries. Optimizing and a…
CHROTRAN: A mathematical and computational model for in situ heavy metal remediation in heterogeneous aquifers
Scott K. Hansen, Sachin Pandey, Satish Karra +1
Groundwater contamination by heavy metals is a critical environmental problem for which in situ remediation is frequently the only viable treatment option. For such interventions,…