Add to ORKGThis report describes an initial investigation into the error convergence trends in sampling-based uncertainty quantification (UQ) studies performed both with and without response surface approximations. The data provided by this limited study indicate that RS-based UQ methods exhibit error trends that are as good or better (converging faster to zero) when compared to conventional sampling-based UQ methods
Several uncertainty propagation algorithms are available in literature: (i) MonteCarlo simulations b...
In this papera, computationally efficient techniques for propagating the effect of uncertainty are d...
THE PAPER BRIEFLY REVIEWS THE EXISTING SAMPLING TECHNIQUES USED FOR MONTE CARLO SIMULATI...
Uncertainty quantification (UQ) is a mainstream research topic in applied mathematics and statistics...
Since 2000, the research of uncertainty quantification (UQ) has been successfully applied in many fi...
Uncertainty has always been an important consideration when designing, analyzing and testing enginee...
Uncertainty quantification (UQ) methods bring rigorous statistical connections to the analysis of co...
Uncertainty Quantification (UQ) is a relatively new research area which describes the methods and ap...
Understanding the effects of uncertainty on modeling has seen an increased focus as engineering disc...
The ability to quantify the uncertainty of complex engineering structures subject to inherent random...
The capability of effectively quantifying the uncertainty associated to a given prediction is an imp...
The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models...
In the last few decades, uncertainty quantification (UQ) methods have been used widely to ensure the...
The topic of Uncertainty Quantification (UQ) has witnessed massive developments in response to the p...
Uncertainty quantification is rapidly becoming a well-established topic in many fields of engineerin...
Several uncertainty propagation algorithms are available in literature: (i) MonteCarlo simulations b...
In this papera, computationally efficient techniques for propagating the effect of uncertainty are d...
THE PAPER BRIEFLY REVIEWS THE EXISTING SAMPLING TECHNIQUES USED FOR MONTE CARLO SIMULATI...
Uncertainty quantification (UQ) is a mainstream research topic in applied mathematics and statistics...
Since 2000, the research of uncertainty quantification (UQ) has been successfully applied in many fi...
Uncertainty has always been an important consideration when designing, analyzing and testing enginee...
Uncertainty quantification (UQ) methods bring rigorous statistical connections to the analysis of co...
Uncertainty Quantification (UQ) is a relatively new research area which describes the methods and ap...
Understanding the effects of uncertainty on modeling has seen an increased focus as engineering disc...
The ability to quantify the uncertainty of complex engineering structures subject to inherent random...
The capability of effectively quantifying the uncertainty associated to a given prediction is an imp...
The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models...
In the last few decades, uncertainty quantification (UQ) methods have been used widely to ensure the...
The topic of Uncertainty Quantification (UQ) has witnessed massive developments in response to the p...
Uncertainty quantification is rapidly becoming a well-established topic in many fields of engineerin...
Several uncertainty propagation algorithms are available in literature: (i) MonteCarlo simulations b...
In this papera, computationally efficient techniques for propagating the effect of uncertainty are d...
THE PAPER BRIEFLY REVIEWS THE EXISTING SAMPLING TECHNIQUES USED FOR MONTE CARLO SIMULATI...