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Search Publications

NIST Authors in Bold

Displaying 1901 - 1925 of 7108

Metrological challenges for measurements of key climatological observables, Part 4: Atmospheric relative humidity

December 15, 2015
Author(s)
Allan H. Harvey, J W. Lovell-Smith, Rainer Feistel, Olaf Hellmuth, Stephanie A. Bell, M Heinonen, J R. Cooper
Water in its three ambient phases plays the central thermodynamic role in the terrestrial climate system. Clouds control Earth’s radiation balance, atmospheric water vapour is the strongest “greenhouse” gas, and non-equilibrium relative humidity at the air

Modeling and Analysis of Disproportionate Collapse of RC Structures

October 31, 2011
Author(s)
Yihai Bao, Hai S. Lew, S K. Kunnath
Collapse analyses of a 10-story reinforced concrete frame building are carried out to investigate the robustness of structural systems against column removal scenarios. Computationally efficient reduced models are developed and used in the analyses. The

Summary of Modeling and Simulation for NIST RoboCrane? Applications

October 1, 1997
Author(s)
E Amatucci, Roger V. Bostelman, Nicholas Dagalakis, T M. Tsai
The Intelligent Systems Division (ISD) at the National Institute of Standards and Technology has been using modeling and simulation and software for conceptual design and prototyping of advanced robotic cranes. RoboCrane¿ concepts have been developed for

Security Advantages and Challenges of 3D Heterogenous Integration

March 6, 2024
Author(s)
Yuntao Liu, Daniel Xing, Isaac McDaniel, Olsan Ozbay, Abir Ahsan Akib, Mumtahina Islam Sukanya, Sanjay (Jay) Rekhi, Ankur Srivastava
Three-dimensional heterogeneous integration offers compelling opportunities to enhance the security and trust in the current semiconductor chain while new attack surfaces may emerge.

Deep learning approaches for time-resolved laser absorptivity prediction

January 5, 2024
Author(s)
Runbo Jiang, John Smith, Yu-Tsen Yi, Tao Sun, Brian Simonds, Anthony D. Rollett
The quantification of the amount of absorbed light is essential for understanding laser-material interactions and melt pool dynamics in order to minimize defects in additive manufactured metal components. The geometry of a vapor depression, also known as a

Analysis of Neural Network Detectors for Network Attacks

November 15, 2023
Author(s)
Qingtian Zou, Lan Zhang, Anoop Singhal, Xiaoyan Sun, Peng Liu
While network attacks play a critical role in many advanced persistent threat (APT) campaigns, an arms race exists between the network defenders and the adversary: to make APT campaigns stealthy, the adversary is strongly motivated to evade the detection

Exploiting redundancy in large materials datasets for efficient machine learning with less data

November 10, 2023
Author(s)
Kamal Choudhary, Brian DeCost, Kangming Li, Daniel "Persaud ", Jason Hattrick-Simpers, Michael Greenwood
Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multiple large datasets for various material properties, by revealing that up to

Benchmarking Active Learning Strategies for Materials Optimization and Discovery

July 9, 2022
Author(s)
Alex Wang, Haotong Liang, Austin McDannald, Ichiro Takeuchi, A. Gilad Kusne
Autonomous physical science is revolutionizing materials science. In these systems, machine learning (ML) controls experiment design, execution and analysis in a closed loop. Active learning, the ML field of optimal experiment design, selects each

Phase Field Benchmark Problems for Nucleation

June 1, 2022
Author(s)
Wenkun Wu, David M. Taboada, Jonathan Guyer, Peter W. Voorhees, James Warren, Daniel Wheeler, Tamas Pusztai, Laszlo Granasy, Olle G. Heinonen
We present nucleation phase field model benchmark problems, expanding on our previous benchmark problems on diffusion, precipitation, dendritic growth, linear elasticity, fluid flow and electrochemistry. Nucleation is the first step in the formation of

Machine Learning-Based Algorithmically Generated Domain Detection

May 1, 2022
Author(s)
Zheng Wang, Yang Guo, Douglas Montgomery
Malware like botnets typically uses domain generation algorithms (DGAs) to dynamically produce a large number of random algorithmically generated domains (AGDs) and use a few of them to communicate with the command and control servers. AGD detection

PrepareForLeap: An automated tool for fast PDB-to-parameter generation

March 23, 2022
Author(s)
Daniel R. Roe, Christina Bergonzo
Setting up molecular dynamics simulations from experimentally determined structures is often complicated by a variety of factors, particularly the inclusion of carbohydrates, since these have several forms which can be linked in a variety of ways. Here we

Recent Advances and Applications of Deep Learning Methods in Materials Science

February 24, 2022
Author(s)
Kamal Choudhary, Brian DeCost, Chi Chen, Anubhav Jain, Francesca Tavazza, Ryan Cohn, Cheol WooPark, Alok Choudhary, Ankit Agrawal, Simon Billinge, Elizabeth Holm, ShyuePing Ong, Chris Wolverton
Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. Deep learning allows analysis of unstructured data and automated
Displaying 1901 - 1925 of 7108
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