TY - JOUR
T1 - Probabilistic Approach to Characterize Quantitative Uncertainty in Numerical Approximations
AU - Chaskalovic, Joel
AU - Assous, Franck
N1 - Publisher Copyright:
© 2017 Vilnius Gediminas Technical University.
PY - 2017/1/2
Y1 - 2017/1/2
N2 - This paper proposes a statistical and probabilistic approach to compare and analyze the errors of two different approximation methods. We introduce the principle of numerical uncertainty in such a process, and we illustrate it by considering the discretization difference between two different approximation orders, e.g., first and second order Lagrangian finite element. Then, we derive a probabilistic approach to define and to qualify equivalent results. We illustrate our approach on a model problem on which we built the two above mentioned finite element approximations. We consider some variables as physical “predictors”, and we characterize how they influence the odds of the approximation methods to be locally “same order accurate”.
AB - This paper proposes a statistical and probabilistic approach to compare and analyze the errors of two different approximation methods. We introduce the principle of numerical uncertainty in such a process, and we illustrate it by considering the discretization difference between two different approximation orders, e.g., first and second order Lagrangian finite element. Then, we derive a probabilistic approach to define and to qualify equivalent results. We illustrate our approach on a model problem on which we built the two above mentioned finite element approximations. We consider some variables as physical “predictors”, and we characterize how they influence the odds of the approximation methods to be locally “same order accurate”.
KW - Big Data
KW - data mining
KW - finite elements
KW - probabilistic models
KW - quantitative uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85009180544&partnerID=8YFLogxK
U2 - 10.3846/13926292.2017.1272499
DO - 10.3846/13926292.2017.1272499
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AN - SCOPUS:85009180544
SN - 1392-6292
VL - 22
SP - 106
EP - 120
JO - Mathematical Modelling and Analysis
JF - Mathematical Modelling and Analysis
IS - 1
ER -