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Found 8 papers, 1 papers with code
A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing
The 20 ML emulators based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptron (MLP), are compared to assess these models.
Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing
First, high-resolution datasets for constructing ROMs are generated by solving anisotropic reaction-diffusion equations using a non-negative finite element formulation for different input parameters.
Using Machine Learning to Discern Eruption in Noisy Environments: A Case Study using CO2-driven Cold-Water Geyser in Chimayo, New Mexico
We show that the classification accuracy using RF on the filtered data is greater than 90\%. These aspects make the proposed ML framework attractive for event discrimination and signal enhancement under noisy conditions, with strong potential for application to monitoring leaks in $\mathrm{CO}_2$ sequestration.
Estimating Failure in Brittle Materials using Graph Theory
In brittle fracture applications, failure paths, regions where the failure occurs and damage statistics, are some of the key quantities of interest (QoI).
Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications
In this paper, five different approaches for reduced-order modeling of brittle fracture in geomaterials, specifically concrete, are presented and compared.
Unsupervised Machine Learning Based on Non-Negative Tensor Factorization for Analyzing Reactive-Mixing
An attractive aspect of the proposed ML method is that it ensures the extracted features are non-negative, which are important to obtain a meaningful deconstruction of the mixing processes.
Regression-based reduced-order models to predict transient thermal output for enhanced geothermal systems
The inputs for ROMs are based on these key sensitive parameters.
Sequential geophysical and flow inversion to characterize fracture networks in subsurface systems
In this approach, we first estimate bounds on the statistics for the DFN fracture orientations using microseismic data.
