Final Technical Report: Mathematical Foundations for Uncertainty Quantification in Materials Design

doi:10.2172/1417749

Title: Final Technical Report: Mathematical Foundations for Uncertainty Quantification in Materials Design

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Abstract

We developed path-wise information theory-based and goal-oriented sensitivity analysis and parameter identification methods for complex high-dimensional dynamics and in particular of non-equilibrium extended molecular systems. The combination of these novel methodologies provided the first methods in the literature which are capable to handle UQ questions for stochastic complex systems with some or all of the following features: (a) multi-scale stochastic models such as (bio)chemical reaction networks, with a very large number of parameters, (b) spatially distributed systems such as Kinetic Monte Carlo or Langevin Dynamics, (c) non-equilibrium processes typically associated with coupled physico-chemical mechanisms, driven boundary conditions, hybrid micro-macro systems, etc. A particular computational challenge arises in simulations of multi-scale reaction networks and molecular systems. Mathematical techniques were applied to in silico prediction of novel materials with emphasis on the effect of microstructure on model uncertainty quantification (UQ). We outline acceleration methods to make calculations of real chemistry feasible followed by two complementary tasks on structure optimization and microstructure-induced UQ.

Authors:

Plechac, Petr ^[1]; Vlachos, Dionisios G. ^[1]

Univ. of Delaware, Newark, DE (United States)

Publication Date:: Tue Jan 23 00:00:00 EST 2018

Research Org.:: Univ. of Delaware, Newark, DE (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)

OSTI Identifier:: 1417749

Report Number(s):: DOE-UDEL-0010549

DOE Contract Number:: SC0010549

Resource Type:: Technical Report

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; uncertainty quantification; sensitivity analysis; sampling algorithms; parallel computing; in silicon prediction of materials

Citation Formats


                    Plechac, Petr, and Vlachos, Dionisios G. Final Technical Report: Mathematical Foundations for Uncertainty Quantification in Materials Design.  United States: N. p., 2018. 
        Web.  doi:10.2172/1417749.

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                    Plechac, Petr, & Vlachos, Dionisios G. Final Technical Report: Mathematical Foundations for Uncertainty Quantification in Materials Design.  United States.  doi:10.2172/1417749.

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                    Plechac, Petr, and Vlachos, Dionisios G. Tue .  
        "Final Technical Report: Mathematical Foundations for Uncertainty Quantification in Materials Design".  United States.  
        doi:10.2172/1417749.  https://www.osti.gov/servlets/purl/1417749.

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@article{osti_1417749,

  title        = {Final Technical Report: Mathematical Foundations for Uncertainty Quantification in Materials Design},

  author       = {Plechac, Petr and Vlachos, Dionisios G.},

  abstractNote = {We developed path-wise information theory-based and goal-oriented sensitivity analysis and parameter identification methods for complex high-dimensional dynamics and in particular of non-equilibrium extended molecular systems. The combination of these novel methodologies provided the first methods in the literature which are capable to handle UQ questions for stochastic complex systems with some or all of the following features: (a) multi-scale stochastic models such as (bio)chemical reaction networks, with a very large number of parameters, (b) spatially distributed systems such as Kinetic Monte Carlo or Langevin Dynamics, (c) non-equilibrium processes typically associated with coupled physico-chemical mechanisms, driven boundary conditions, hybrid micro-macro systems, etc. A particular computational challenge arises in simulations of multi-scale reaction networks and molecular systems. Mathematical techniques were applied to in silico prediction of novel materials with emphasis on the effect of microstructure on model uncertainty quantification (UQ). We outline acceleration methods to make calculations of real chemistry feasible followed by two complementary tasks on structure optimization and microstructure-induced UQ.},

  doi          = {10.2172/1417749},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Jan 23 00:00:00 EST 2018},

  month        = {Tue Jan 23 00:00:00 EST 2018}

}

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