Analysis of Agricultural and Engineering Systems Using Simulation Decomposition

Yen-Chen Liu, Leifur Leifsson, Anna Pietrenko-Dabrowska, Slawomir Koziel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This paper focuses on the analysis of agricultural and engineering processes using simulation decomposition (SD). SD is a technique that utilizes Monte Carlo simulations and distribution decomposition to visually evaluate the source and the outcome of different portions of data. Here, SD is applied to three distinct processes: a model problem, a nondestructive evaluation testing system, and an agricultural food-water-energy system. The results demonstrate successful implementations of SD for the different systems, and the illustrate the potential of SD to support new understanding of cause and effect relationships in complex systems.

Original languageEnglish
Title of host publicationComputational Science - ICCS 2022, 22nd International Conference, Proceedings
EditorsDerek Groen, Clélia de Mulatier, Valeria V. Krzhizhanovskaya, Peter M.A. Sloot, Maciej Paszynski, Jack J. Dongarra
Number of pages10
Publication statusPublished - 1 Jan 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13352 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

Funding Information:
Acknowledgements. This material is based upon work supported by the United States National Science Foundation under grant no. 1739551.

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Other keywords

  • Food-water-energy systems
  • Nondestructive evaluation
  • Parameter variability
  • Physics-based simulations
  • Simulation decomposition


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