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02/24/2020
A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing
Accurate predictions of reactive mixing are critical for many Earth and ...
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09/04/2019
PFLOTRAN-SIP: A PFLOTRAN Module for Simulating Spectral-Induced Polarization of Electrical Impedance Data
Spectral induced polarization (SIP) is a non-intrusive geophysical metho...
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08/28/2019
Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing
This paper presents a physics-informed machine learning (ML) framework t...
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10/01/2018
Using Machine Learning to Discern Eruption in Noisy Environments: A Case Study using CO2-driven Cold-Water Geyser in Chimayo, New Mexico
We present an approach based on machine learning (ML) to distinguish eru...
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07/30/2018
Estimating Failure in Brittle Materials using Graph Theory
In brittle fracture applications, failure paths, regions where the failu...
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06/05/2018
Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications
In this paper, five different approaches for reduced-order modeling of b...
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05/16/2018