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Optimization of Thermal Conductance at Interfaces Using Machine Learning Algorithms

https://doi.org/10.1021/acsami.1c23222

Rustam, Sabiha; Schram, Malachi; Lu, Zexi; Chaka, Anne M.; Rosenthal, W. Steven; Pfaendtner, Jim (July 2022, ACS Applied Materials & Interfaces)

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Colloquium : Machine learning in nuclear physics

https://doi.org/10.1103/RevModPhys.94.031003

Boehnlein, Amber; Diefenthaler, Markus; Sato, Nobuo; Schram, Malachi; Ziegler, Veronique; Fanelli, Cristiano; Hjorth-Jensen, Morten; Horn, Tanja; Kuchera, Michelle P.; Lee, Dean; et al (September 2022, Reviews of Modern Physics)

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Real-time artificial intelligence for accelerator control: A study at the Fermilab Booster

https://doi.org/10.1103/PhysRevAccelBeams.24.104601

St. John, Jason; Herwig, Christian; Kafkes, Diana; Mitrevski, Jovan; Pellico, William A.; Perdue, Gabriel N.; Quintero-Parra, Andres; Schupbach, Brian A.; Seiya, Kiyomi; Tran, Nhan; et al (October 2021, Physical Review Accelerators and Beams)
null (Ed.)
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A.I. for nuclear physics

https://doi.org/10.1140/epja/s10050-020-00290-x

Bedaque, Paulo; Boehnlein, Amber; Cromaz, Mario; Diefenthaler, Markus; Elouadrhiri, Latifa; Horn, Tanja; Kuchera, Michelle; Lawrence, David; Lee, Dean; Lidia, Steven; et al (March 2021, The European Physical Journal A)
null (Ed.)
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Identifying build orientation of 3D ‐printed materials using convolutional neural networks

https://doi.org/10.1002/sam.11497

Strube, Jan; Schram, Malachi; Rustam, Sabiha; Kennedy, Zachary; Varga, Tamas (January 2021, Statistical Analysis and Data Mining: The ASA Data Science Journal)

Abstract The advent of additive manufacturing (AM) processes brought with it intense research into various materials and manufacturing processes. At the same time, the need for validation of material properties, as well as study and forecasting of aging, has arisen. Modern imaging techniques, like X‐ray computed tomography (XCT), are a convenient vehicle for such studies; however, the large datasets they produce require novel analysis techniques to efficiently extract critical information. In this paper, we present our work on developing a 3D extension of the ResNet architecture to distinguish between two build orientations of tensile bars produced by AM. Using only information from XCT, our method achieves a 99.3% correct classification at a misidentification of 1%.
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