School of Engineering and Applied Science

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Department of Electrical and Computer Engineering

Department of Materials Science & Engineering

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1 to 10 of 61 Results

Particle Image Velocimetry for Bio-Inspired Vortex, Fin, and Boundary Interactions Apr 30, 2026 Zhu, Yuanhang; Ormonde, Pedro C.; Liu, Leo; Pan, Yu; Westfall, Elizabeth; Han, Tianjun; Zhu, Joseph; Zhong, Qiang; Bart-Smith, Hilary; Dong, Haibo; Lauder, George V.; Moored, Keith W.; Quinn, Daniel B., 2025, "Particle Image Velocimetry for Bio-Inspired Vortex, Fin, and Boundary Interactions", https://doi.org/10.18130/V3/UL6CJE, University of Virginia Dataverse, V4 Particle Image Velocimetry for Bio-Inspired Vortex, Fin, and Boundary Interactions.
A global dataset of household-level water, sanitation, and hygiene (WASH) predicted conditions Apr 22, 2026 Fang, Bin, 2026, "A global dataset of household-level water, sanitation, and hygiene (WASH) predicted conditions", https://doi.org/10.18130/V3/O58DEE, University of Virginia Dataverse, V3 The dataset includes GeoTIFF raster layers representing the predicted probability of each ordinal class. Files are named using a standardized convention that encodes the WASH component and ordinal class name.
Code for modeling WASH components by RTMB Apr 22, 2026 Fang, Bin, 2026, "Code for modeling WASH components by RTMB", https://doi.org/10.18130/V3/ED1WNW, University of Virginia Dataverse, V3 The repository contains two R code files: data preprocessing and model/prediction/evaluation files.
Audio PhD Dissertation of Nicholas Gardella \| Responsible and Equitable Use of AI Code Generators in Computer Science Education Apr 21, 2026 Gardella, Nicholas, 2026, "Audio PhD Dissertation of Nicholas Gardella \| Responsible and Equitable Use of AI Code Generators in Computer Science Education", https://doi.org/10.18130/V3/VM1IPO, University of Virginia Dataverse, V1 This is the archival audiobook version of the PhD Dissertation of Nicholas Gardella, Responsible and Equitable Use of AI Code Generators in Computer Science Education.
RAISE (Retrofitting Allocation using Income & Spatial Equity): A Tunable Strategy for Household Retrofitting Allocations Apr 20, 2026 - Department of Computer Science Kishore, Aparna, 2026, "RAISE (Retrofitting Allocation using Income & Spatial Equity): A Tunable Strategy for Household Retrofitting Allocations", https://doi.org/10.18130/V3/BHIB7J, University of Virginia Dataverse, V1, UNF:6:1Z/FcgeIHU7PJ2+7ISL3Dw== [fileUNF] The dataset includes both the code and output data for the research work on the retrofitting optimization framework called RAISE. RAISE is designed to allocate state retrofitting budgets to households in a way that maximizes energy savings while ensuring a balanced distribution of funds across counties and income groups. The input folder contains t...
Replication Data for: Thermal Transport in γ-InSe: Bulk Single Crystals and Thin Flakes Apr 11, 2026 - Department of Electrical and Computer Engineering Tonni, Farjana Ferdous, 2026, "Replication Data for: Thermal Transport in γ-InSe: Bulk Single Crystals and Thin Flakes", https://doi.org/10.18130/V3/TJTASK, University of Virginia Dataverse, V1 This dataset was created to document experimentally measured thermal and thermoelectric transport properties of layered metal chalcogenide thin films under controlled conditions. It includes temperature-dependent in-plane thermal conductivity, sample thickness, and supporting structural and compositional characterization data. The dataset enables a...
Data for: Mechanistic insights into laser-generated surface nanomorphology from physics-guided, explainable machine learning and molecular dynamics simulations Apr 3, 2026 Valavanis, Antonios S.; Gurevich, Evgeny L.; Shugaev, Maxim V.; Zhigilei, Leonid V., 2026, "Data for: Mechanistic insights into laser-generated surface nanomorphology from physics-guided, explainable machine learning and molecular dynamics simulations", https://doi.org/10.18130/V3/XCKLVR, University of Virginia Dataverse, V2 Data for: Mechanistic insights into laser-generated surface nanomorphology from physics-guided, explainable machine learning and molecular dynamics simulations
Data for: A Physics-Regularized Machine Learning Approach for Predicting Time–Temperature–Transformation Curves in Alloys: Application to Uranium-Based Alloys Mar 30, 2026 - Materials Informatics Group Garg, Sunidhi; Jishnu Bhattacharyya; Vineet V. Joshi; Sean R. Agnew; Prasanna V. Balachandran, 2026, "Data for: A Physics-Regularized Machine Learning Approach for Predicting Time–Temperature–Transformation Curves in Alloys: Application to Uranium-Based Alloys", https://doi.org/10.18130/V3/EXJA3W, University of Virginia Dataverse, V1 The data in each sheet is described below: 1. Data_train: Training dataset used for all the ML models 2. Data_test: Test dataset used for all the ML models 3. Data_virtual: Dataset having the principal component (PC) values of U-Mo-X alloys absent from train and test set and is the input to predict the TTT curves for these U-Mo-X alloys 4. Data_cal...
Data for: An Active Learning Workflow for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys Mar 23, 2026 - Materials Informatics Group Liu, Shunshun; Balachandran, Prasanna V., 2026, "Data for: An Active Learning Workflow for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys", https://doi.org/10.18130/V3/AD6H08, University of Virginia Dataverse, V1 Data for: An Active Learning Framework for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys. This repository contains relaxation trajectories for all BCC HEA crystal structures, and 11 template SQS structures.
Materials Informatics Group Mar 19, 2026Department of Materials Science & Engineering

Particle Image Velocimetry for Bio-Inspired Vortex, Fin, and Boundary Interactions

Apr 30, 2026

Zhu, Yuanhang; Ormonde, Pedro C.; Liu, Leo; Pan, Yu; Westfall, Elizabeth; Han, Tianjun; Zhu, Joseph; Zhong, Qiang; Bart-Smith, Hilary; Dong, Haibo; Lauder, George V.; Moored, Keith W.; Quinn, Daniel B., 2025, "Particle Image Velocimetry for Bio-Inspired Vortex, Fin, and Boundary Interactions", https://doi.org/10.18130/V3/UL6CJE, University of Virginia Dataverse, V4

Particle Image Velocimetry for Bio-Inspired Vortex, Fin, and Boundary Interactions.

A global dataset of household-level water, sanitation, and hygiene (WASH) predicted conditions

Apr 22, 2026

Fang, Bin, 2026, "A global dataset of household-level water, sanitation, and hygiene (WASH) predicted conditions", https://doi.org/10.18130/V3/O58DEE, University of Virginia Dataverse, V3

The dataset includes GeoTIFF raster layers representing the predicted probability of each ordinal class. Files are named using a standardized convention that encodes the WASH component and ordinal class name.

Code for modeling WASH components by RTMB

Apr 22, 2026

Fang, Bin, 2026, "Code for modeling WASH components by RTMB", https://doi.org/10.18130/V3/ED1WNW, University of Virginia Dataverse, V3

The repository contains two R code files: data preprocessing and model/prediction/evaluation files.

Audio PhD Dissertation of Nicholas Gardella | Responsible and Equitable Use of AI Code Generators in Computer Science Education

Apr 21, 2026

Gardella, Nicholas, 2026, "Audio PhD Dissertation of Nicholas Gardella | Responsible and Equitable Use of AI Code Generators in Computer Science Education", https://doi.org/10.18130/V3/VM1IPO, University of Virginia Dataverse, V1

This is the archival audiobook version of the PhD Dissertation of Nicholas Gardella, Responsible and Equitable Use of AI Code Generators in Computer Science Education.

RAISE (Retrofitting Allocation using Income & Spatial Equity): A Tunable Strategy for Household Retrofitting Allocations

Apr 20, 2026 - Department of Computer Science

Kishore, Aparna, 2026, "RAISE (Retrofitting Allocation using Income & Spatial Equity): A Tunable Strategy for Household Retrofitting Allocations", https://doi.org/10.18130/V3/BHIB7J, University of Virginia Dataverse, V1, UNF:6:1Z/FcgeIHU7PJ2+7ISL3Dw== [fileUNF]

The dataset includes both the code and output data for the research work on the retrofitting optimization framework called RAISE. RAISE is designed to allocate state retrofitting budgets to households in a way that maximizes energy savings while ensuring a balanced distribution of funds across counties and income groups. The input folder contains t...

Replication Data for: Thermal Transport in γ-InSe: Bulk Single Crystals and Thin Flakes

Apr 11, 2026 - Department of Electrical and Computer Engineering

Tonni, Farjana Ferdous, 2026, "Replication Data for: Thermal Transport in γ-InSe: Bulk Single Crystals and Thin Flakes", https://doi.org/10.18130/V3/TJTASK, University of Virginia Dataverse, V1

This dataset was created to document experimentally measured thermal and thermoelectric transport properties of layered metal chalcogenide thin films under controlled conditions. It includes temperature-dependent in-plane thermal conductivity, sample thickness, and supporting structural and compositional characterization data. The dataset enables a...

Data for: Mechanistic insights into laser-generated surface nanomorphology from physics-guided, explainable machine learning and molecular dynamics simulations

Apr 3, 2026

Valavanis, Antonios S.; Gurevich, Evgeny L.; Shugaev, Maxim V.; Zhigilei, Leonid V., 2026, "Data for: Mechanistic insights into laser-generated surface nanomorphology from physics-guided, explainable machine learning and molecular dynamics simulations", https://doi.org/10.18130/V3/XCKLVR, University of Virginia Dataverse, V2

Data for: Mechanistic insights into laser-generated surface nanomorphology from physics-guided, explainable machine learning and molecular dynamics simulations

Data for: A Physics-Regularized Machine Learning Approach for Predicting Time–Temperature–Transformation Curves in Alloys: Application to Uranium-Based Alloys

Mar 30, 2026 - Materials Informatics Group

Garg, Sunidhi; Jishnu Bhattacharyya; Vineet V. Joshi; Sean R. Agnew; Prasanna V. Balachandran, 2026, "Data for: A Physics-Regularized Machine Learning Approach for Predicting Time–Temperature–Transformation Curves in Alloys: Application to Uranium-Based Alloys", https://doi.org/10.18130/V3/EXJA3W, University of Virginia Dataverse, V1

The data in each sheet is described below: 1. Data_train: Training dataset used for all the ML models 2. Data_test: Test dataset used for all the ML models 3. Data_virtual: Dataset having the principal component (PC) values of U-Mo-X alloys absent from train and test set and is the input to predict the TTT curves for these U-Mo-X alloys 4. Data_cal...

Data for: An Active Learning Workflow for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys

Mar 23, 2026 - Materials Informatics Group

Liu, Shunshun; Balachandran, Prasanna V., 2026, "Data for: An Active Learning Workflow for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys", https://doi.org/10.18130/V3/AD6H08, University of Virginia Dataverse, V1

Data for: An Active Learning Framework for Predicting Misfit Volume in Body-Centered Cubic Refractory High-Entropy Alloys. This repository contains relaxation trajectories for all BCC HEA crystal structures, and 11 template SQS structures.

Materials Informatics Group

Mar 19, 2026Department of Materials Science & Engineering

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