An official website of the United States government Here's how you know

Official websites use .gov

A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS

A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Attention:

The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 7:00 AM ET to 7:30 AM ET on Friday, April 24 due to maintenance. We apologize for the inconvenience.

Search for: All records

Creators/Authors contains: "Schram, Malachi"

« Prev Next »

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding using a seq2seq LSTM Surrogate Model for Real-Time Application in a Coastal-Urban City

https://doi.org/10.1016/j.jhydrol.2025.132697

Roy, Binata; Goodall, Jonathan L; McSpadden, Diana; Goldenberg, Steven; Schram, Malachi (August 2025, Journal of Hydrology)

Full Text Available
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)

Full Text Available
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)
null (Ed.)
Full Text Available
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)

Full Text Available
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.)
Full Text Available
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.)
Full Text Available