144 citations · 386 across the 15 of their papers we have counts for
16 papers
GeoThermalCloud: Machine Learning for Geothermal Resource Exploration
Maruti K. Mudunuru, Velimir V. Vesselinov, Bulbul Ahmmed
This paper presents a novel ML-based methodology for geothermal exploration towards PFA applications. Our methodology is provided through our open-source ML framework, GeoThermalCl…
Nonequilibrium Statistical Mechanics and Optimal Prediction of Partially-Observed Complex Systems
Adam Rupe, Velimir V. Vesselinov, James P. Crutchfield
Only a subset of degrees of freedom are typically accessible or measurable in real-world systems. As a consequence, the proper setting for empirical modeling is that of partially-o…
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…
Learning to regularize with a variational autoencoder for hydrologic inverse analysis
Daniel O'Malley, John K. Golden, Velimir V. Vesselinov
Inverse problems often involve matching observational data using a physical model that takes a large number of parameters as input. These problems tend to be under-constrained and…
Unsupervised Machine Learning Based on Non-Negative Tensor Factorization for Analyzing Reactive-Mixing
V. V. Vesselinov, M. K. Mudunuru, S. Karra +2
Analysis of reactive-diffusion simulations requires a large number of independent model runs. For each high-fidelity simulation, inputs are varied and the predicted mixing behavior…
Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clustering
Valentin Stanev, Velimir V. Vesselinov, A. Gilad Kusne +3
Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries. Optimizing and a…
Nonnegative/binary matrix factorization with a D-Wave quantum annealer
Daniel O'Malley, Velimir V. Vesselinov, Boian S. Alexandrov +1
D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest, but have been used for few real-world computations. Machine learning…
Characterizing the impact of model error in hydrogeologic time series recovery inverse problems
Scott K. Hansen, Jiachuan He, Velimir V. Vesselinov
Hydrogeologic models are commonly over-smoothed relative to reality, owing to the difficulty of obtaining accurate high-resolution information about the subsurface. When used in an…
CHROTRAN: A mathematical and computational model for in situ heavy metal remediation in heterogeneous aquifers
Scott K. Hansen, Sachin Pandey, Satish Karra +1
Groundwater contamination by heavy metals is a critical environmental problem for which in situ remediation is frequently the only viable treatment option. For such interventions,…
Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals
Filip L. Iliev, Valentin G. Stanev, Velimir V. Vesselinov +1
Factor analysis is broadly used as a powerful unsupervised machine learning tool for reconstruction of hidden features in recorded mixtures of signals. In the case of a linear appr…
Identification of release sources in advection-diffusion system by machine learning combined with Green function inverse method
Valentin G. Stanev, Filip L. Iliev, Scott Hansen +2
The identification of sources of advection-diffusion transport is based usually on solving complex ill-posed inverse models against the available state- variable data records. Howe…
Robust decision analysis for environmental management of groundwater contamination sites
Velimir V. Vesselinov, Daniel O'Malley, Danny Katzman
In contrast to many other engineering fields, the uncertainties in subsurface processes (e.g., fluid flow and contaminant transport in aquifers) and their parameters are notoriousl…
Adaptive hybrid optimization strategy for calibration and parameter estimation of physical models
Velimir V. Vesselinov, Dylan R. Harp
A new adaptive hybrid optimization strategy, entitled squads, is proposed for complex inverse analysis of computationally intensive physical models. The new strategy is designed to…
Contaminant remediation decision analysis using information gap theory
Dylan R. Harp, Velimir V. Vesselinov
Decision making under severe lack of information is a ubiquitous situation in nearly every applied field of engineering, policy, and science. A severe lack of information precludes…
Unified Analytical Solution for Radial Flow to a Well in a Confined Aquifer
Phoolendra Kumar Mishra, Velimir V. Vesselinov
Drawdowns generated by extracting water from a large diameter (e.g. water supply) well are affected by wellbore storage. We present an analytical solution in Laplace transformed sp…
Accounting for the influence of aquifer heterogeneity on spatial propagation of pumping drawdown
Dylan R. Harp, Velimir V. Vesselinov
It has been previously observed that during a pumping test in heterogeneous media, drawdown data from different time periods collected at a single location produce different estima…