Standout Papers

Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning 2019 2026 2021 2023 315
  1. Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning (2019)
    Amy McGovern, Ryan Lagerquist et al. Bulletin of the American Meteorological Society

Immediate Impact

28 from Science/Nature 73 standout
Sub-graph 1 of 22

Citing Papers

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems
2024 Standout
The Rise of Data-Driven Weather Forecasting: A First Statistical Assessment of Machine Learning–Based Weather Forecasts in an Operational-Like Context
2024 Standout
5 intermediate papers

Works of Travis M. Smith being referenced

Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning
2019 Standout
Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather
2017

Author Peers

Author Last Decade Papers Cites
Travis M. Smith 2140 1830 610 45 2.7k
Valliappa Lakshmanan 1828 1486 480 60 2.2k
Kimberly L. Elmore 1618 1392 438 54 2.1k
Tatiana G. Smirnova 2382 2174 492 28 2.7k
Gregory J. Stumpf 1809 1391 483 25 2.1k
John M. Brown 2794 2514 672 56 3.9k
Ming Hu 2058 1711 450 40 2.3k
Chris G. Tzanis 1007 1062 610 72 1.9k
William D. Hall 3488 3205 473 53 4.2k
Stephen S. Weygandt 2128 1885 465 24 2.4k
David C. Dowell 3645 3051 917 59 3.9k

All Works

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2026