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Lori Graham-Brady

Johns Hopkins University

Baltimore, MD 20036-1984

United States

SCHOLARLY PAPERS

5

DOWNLOADS

369

TOTAL CITATIONS

4

Scholarly Papers (5)

1.

Heteroscedastic Gaussian Process Regression for Material Structure-Property Relationship Modeling

Number of pages: 27 Posted: 15 Jun 2024
Ozge Ozbayram, Audrey Olivier and Lori Graham-Brady
Johns Hopkins University, University of Southern California - Sonny Astani Department of Civil and Environmental Engineering and Johns Hopkins University
Downloads 154 (494,203)
Citation 4

Abstract:

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Gaussian process regression, heteroscedasticity, uncertainty quantification, materials modeling

2.

Uncertainty-Aware Multi-Output Gaussian-Process Autoregression for Melt-Pool Dynamics and Microstructural Implications

Number of pages: 24 Posted: 25 Nov 2025
Government of the United States of America - Idaho National Laboratory, Government of the United States of America - Idaho National Laboratory, Government of the United States of America - Idaho National Laboratory, Johns Hopkins University and Government of the United States of America - Idaho National Laboratory
Downloads 95 (742,061)

Abstract:

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Gaussian processes, Uncertainty quantification, Advanced manufacturing, Phase-field, Arbitrary Lagrangian-Eulerian

3.

Batch Active Learning for Microstructure-Property Relations in Energetic Materials

Number of pages: 25 Posted: 20 Oct 2024
Johns Hopkins University, Georgia Institute of Technology, Georgia Institute of Technology, Georgia Institute of Technology - George W. Woodruff School of Mechanical Engineering, Sandia National Laboratories, Georgia Institute of Technology - George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology and Johns Hopkins University
Downloads 52 (1,074,648)

Abstract:

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batch active learning, multi-output gaussian process regression, polymer-bonded explosives, materials modeling

4.

Active Learning with Heteroscedastic Gaussian Process Regression Model

Number of pages: 29 Posted: 17 Mar 2026
Ozge Ozbayram, Audrey Olivier and Lori Graham-Brady
Johns Hopkins University, University of Southern California - Sonny Astani Department of Civil and Environmental Engineering and Johns Hopkins University
Downloads 44 (1,304,549)

Abstract:

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gaussian process regression, heteroscedasticity, active learning, Uncertainty quantification, materials modeling

5.

A Hybrid Conditional Diffusion-DeepONet Framework for High-Fidelity Stress Prediction in Hyperelastic Materials

Number of pages: 23 Posted: 20 Mar 2026
Government of the United States of America - Idaho National Laboratory, affiliation not provided to SSRN, Johns Hopkins University and Johns Hopkins University
Downloads 24 (1,424,331)

Abstract:

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DDPM, DeepONet, Hyperelastic materials, Neural Operators, Diffusion Models