We assess a method of quantification of margins and uncertainties (QMU) in applications where the main source of uncertainty is an imperfect knowledge or characterization of the material behavior. The aim of QMU is to determine adequate design margins given quantified uncertainties and a desired level of confidence in the design. We quantify uncertainties through rigorous probability bounds computed by exercising an existing deterministic code in order to sample the mean response and identify worst-case combinations of parameters. The resulting methodology is non-intrusive and can be wrapped around existing solvers. The use of rigorous probability bounds ensures that the resulting designs are conservative to within a desired level of confid...
Verification and validation (V&V) are processes designed to assure, with quantifiable confidence...
International audienceThis book results from a course developed by the author and reflects both his ...
Uncertainty quantification and its propagation across multi-scale model/experiment chains are key el...
We assess a method of quantification of margins and uncertainties (QMU) in applications where the ma...
This work is concerned with establishing the feasibility of a data-on-demand (DoD) uncertainty quant...
The design and development of cutting-edge light materials for extreme conditions including high-spe...
The objective of this paper is to implement Dempster–Shafer Theory of Evidence (DSTE) in the presenc...
This Part II of this series is concerned with establishing the feasibility of an extended data-on-de...
The objective of this paper is to implement Dempster-Shafer Theory of Evidence (DSTE) in the presenc...
Uncertainties related to the material properties of a composite material can be determined from the ...
The Finite Element Method possess the intrinsic characteristic that, due to the assumptions made for...
In 2001, the National Nuclear Security Administration of the U.S. Department of Energy in conjunctio...
Phenomenological material models such as Johnson-Cook plasticity are often used in finite element si...
Several uncertainty propagation algorithms are available in literature: (i) MonteCarlo simulations b...
Robust optimization is being used in metal forming processes to select the design which is least sen...
Verification and validation (V&V) are processes designed to assure, with quantifiable confidence...
International audienceThis book results from a course developed by the author and reflects both his ...
Uncertainty quantification and its propagation across multi-scale model/experiment chains are key el...
We assess a method of quantification of margins and uncertainties (QMU) in applications where the ma...
This work is concerned with establishing the feasibility of a data-on-demand (DoD) uncertainty quant...
The design and development of cutting-edge light materials for extreme conditions including high-spe...
The objective of this paper is to implement Dempster–Shafer Theory of Evidence (DSTE) in the presenc...
This Part II of this series is concerned with establishing the feasibility of an extended data-on-de...
The objective of this paper is to implement Dempster-Shafer Theory of Evidence (DSTE) in the presenc...
Uncertainties related to the material properties of a composite material can be determined from the ...
The Finite Element Method possess the intrinsic characteristic that, due to the assumptions made for...
In 2001, the National Nuclear Security Administration of the U.S. Department of Energy in conjunctio...
Phenomenological material models such as Johnson-Cook plasticity are often used in finite element si...
Several uncertainty propagation algorithms are available in literature: (i) MonteCarlo simulations b...
Robust optimization is being used in metal forming processes to select the design which is least sen...
Verification and validation (V&V) are processes designed to assure, with quantifiable confidence...
International audienceThis book results from a course developed by the author and reflects both his ...
Uncertainty quantification and its propagation across multi-scale model/experiment chains are key el...