L
Luther W. White
Researcher at University of Oklahoma
Publications - 108
Citations - 1517
Luther W. White is an academic researcher from University of Oklahoma. The author has contributed to research in topics: Estimator & Optimal control. The author has an hindex of 16, co-authored 106 publications receiving 1388 citations. Previous affiliations of Luther W. White include Idaho State University.
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Probabilistic inversion of a terrestrial ecosystem model: Analysis of uncertainty in parameter estimation and model prediction
TL;DR: In this paper, a Bayesian probability inversion and a Markov chain Monte Carlo (MCMC) technique were applied to a terrestrial ecosystem model to analyze uncertainties of estimated carbon (C) transfer coefficients and simulated C pool sizes.
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Sustainability of terrestrial carbon sequestration: A case study in Duke Forest with inversion approach
Yiqi Luo,Luther W. White,Josep G. Canadell,Evan H. DeLucia,David S. Ellsworth,Adrien C. Finzi,John Lichter,William H. Schlesinger +7 more
TL;DR: In this article, the authors developed a conceptual framework to define the sustainability of terrestrial carbon (C) sequestration based on C influx and residence time (τ), which quantifies the capacity for C storage in various plant and soil pools.
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A general class of branch-and-bound methods in global optimization with some new approaches for concave minimization
K. A. Grasse,Luther W. White +1 more
TL;DR: Based on a review of existing algorithms, a general branch-and-bound concept in global optimization is presented and a broad class of realizations are derived that include existing and several new approaches for concave minimization problems.
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Elevated co2 differentiates ecosystem carbon processes: deconvolution analysis of duke forest face data
Yiqi Luo,Lianhai Wu,Jeffrey A. Andrews,Luther W. White,Roser Matamala,Karina V. R. Schäfer,William H. Schlesinger +6 more
TL;DR: In this paper, a deconvolution analysis was used to differentiate C flux pathways in forest soils and to quantify the flux through those pathways, and the analysis indicated that the fine-root turnover is a major process adding C to the rhizosphere.
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Neural Network Model for Asphalt Concrete Permeability
Rafiqul A. Tarefder,Rafiqul A. Tarefder,Luther W. White,Luther W. White,Musharraf Zaman,Musharraf Zaman +5 more
TL;DR: In this article, a four-layer feed-forward neural network is constructed and applied to determine a mapping associating mix design and testing factors of asphalt concrete samples with their performance in conductance to flow or permeability.