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Sharlotte Kramer

Sandia National Laboratories

P.O. Box 969

Livermore, CA 94551

United States

  • Sandia National Laboratories

SCHOLARLY PAPERS

3

DOWNLOADS

165

TOTAL CITATIONS

4

Scholarly Papers (3)

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1.

Deep-Learned Generators of Porosity Distributions Produced During Metal Additive Manufacturing

Number of pages: 33 Posted: 15 May 2022
Odinakachukwu Francis Ogoke, Kyle Johnson, Michael Glinsky, Christopher Martin Laursen, Sharlotte Kramer and Amir Barati Farimani
Carnegie Mellon University, Sandia National Laboratories, Sandia National Laboratories, Sandia National Laboratories, Sandia National Laboratories and Carnegie Mellon University
Downloads 107 (654,668)
Citation 4

Abstract:

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Deep learning, Microstructural analysis, Generative Adversarial Networks, Porosity

2.

Multiscale Simulation of Spatially Correlated Microstructure Via a Latent Space Representation

Number of pages: 23 Posted: 04 Jun 2024
Reese Jones, Craig M. Hamel, Dan S. Bolintineanu, Kyle Johnson, Robert Buarque de Macedo, Jan Fuhg, Nikolaos Bouklas and Sharlotte Kramer
Sandia National Laboratories, Sandia National Laboratories, Sandia National Laboratories, Sandia National Laboratories, Sandia National Laboratories, University of Texas at Austin, Cornell University - Sibley School of Mechanical and Aerospace Engineering and Sandia National Laboratories
Downloads 31 (1,318,317)

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Multiscale simulation, elastoplasticity, spatial correlation, finite-size effects, latent space, autoencoder

3.

Uncertainty Quantification of Neural Network Models of Evolving Processes Via Langevin Sampling

Number of pages: 23 Posted: 03 Jun 2025
Reese Jones, Cosmin Safta, Ravi G. Patel, Raelynn Wonnacot, Dan S. Bolintineanu, Craig M. Hamel and Sharlotte Kramer
Sandia National Laboratories, affiliation not provided to SSRN, Sandia National Laboratories, University of Maryland, College Park, Sandia National Laboratories, Sandia National Laboratories and Sandia National Laboratories
Downloads 27 (1,359,097)

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Uncertainty quantification, variational inference, Langevin sampling, neural networks, score function, Bayesian last layer.