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…
Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing
M. K. Mudunuru, S. Karra
This paper presents a physics-informed machine learning (ML) framework to construct reduced-order models (ROMs) for reactive-transport quantities of interest (QoIs) based on high-f…
Using Machine Learning to Discern Eruption in Noisy Environments: A Case Study using CO2-driven Cold-Water Geyser in Chimayo, New Mexico
B. Yuan, Y. J. Tan, M. K. Mudunuru +8
We present an approach based on machine learning (ML) to distinguish eruption and precursory signals of Chimayó geyser (New Mexico, USA) under noisy environments. This geyser can b…
Estimating Failure in Brittle Materials using Graph Theory
M. K. Mudunuru, N. Panda, S. Karra +5
In brittle fracture applications, failure paths, regions where the failure occurs and damage statistics, are some of the key quantities of interest (QoI). High-fidelity models for…