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Los Alamos Science No. 29, 2005: Predicting Risks in the Earth Sciences : Volcanological Examples

By Greg Valentine

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Book Id: WPLBN0002113710
Format Type: PDF eBook :
File Size: 1.5 MB
Reproduction Date: 2005
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Title: Los Alamos Science No. 29, 2005: Predicting Risks in the Earth Sciences : Volcanological Examples  
Author: Greg Valentine
Volume: Volume 29, Article 5
Language: English
Subject: Newsletter, Science, Education
Collections: Periodicals: Journal and Magazine Collection, Los Alamos Science, Los Alamos National Laboratory Journal Collection
Publication Date:
Publisher: Los Alamos National Laboratory


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Valentine, G. (2005). Los Alamos Science No. 29, 2005. Retrieved from

Description: Improving predictive capability is an implicit goal in the major missions of Los Alamos National Laboratory: simulating performance of weapons in the stockpile, quantifying uncertainties in those simulations, and developing strategies to mitigate global threats. Success in achieving this goal depends on closely coordinating theory, experiment, and computer simulation.

Table of Contents
Table of Contents: The Evolving Deterrent -- Error Analysis and Simulations of Complex Phenomena -- Reducing Uncertainty in Nuclear Data -- The Ocean Perspective: Uncertainties in Climate Prediction -- Predicting Risks in the Earth Sciences: Volcanological Examples -- Quantum Molecular Dynamics: Simulating Warm, Dense Matter -- Predicting Material Strength, Damage, and Fracture: The Synergy between Experiment and Modeling -- Complex Networks: The Challenge of Interaction Topology -- Models of the Retina with Application to the Design of a Visual Prosthesis -- The Turbulence Problem: An Experimentalist's Perspective -- Direct Numerical Simulations of Turbulence: Data Generation and Statistical Analysis -- The LANS-alpha Model for Computing Turbulence: Origins, Results, and Open Problems -- Taylor's Hypothesis, Hamilton's Principle, and the LANS-alpha Model for Computing Turbulence -- Field Theory and Statistical Hydrodynamics: The First Analytical Predictions of Anomalous Scaling -- Physically Motivated Discretization Methods: A Strategy for Increased Predictiveness


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