10 citations · 21 across the 3 of their papers we have counts for
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cs.CE2019
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…
cs.CE2018
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…