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03/09/2023
CoolPINNs: A Physics-informed Neural Network Modeling of Active Cooling in Vascular Systems
Emerging technologies like hypersonic aircraft, space exploration vehicl...
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10/06/2021
SWAT Watershed Model Calibration using Deep Learning
Watershed models such as the Soil and Water Assessment Tool (SWAT) consi...
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01/11/2021
A deep learning modeling framework to capture mixing patterns in reactive-transport systems
Prediction and control of chemical mixing are vital for many scientific ...
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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