We present an optimal uncertainty quantification (OUQ) protocol for systems that are characterized by an existing physics-based model and for which only legacy data is available, i.e., no additional experimental testing of the system is possible. Specifically, the OUQ strategy developed in this work consists of using the legacy data to establish, in a probabilistic sense, the level of error of the model, or modeling error, and to subsequently use the validated model as a basis for the determination of probabilities of outcomes. The quantification of modeling uncertainty specifically establishes, to a specified confidence, the probability that the actual response of the system lies within a certain distance of the model. Once the extent of m...
The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models...
In the real world, a significant challenge faced in designing critical systems is the lack of availa...
The starting point in uncertainty quantification is a stochastic model, which is fitted to a technic...
We present an optimal uncertainty quantification (OUQ) protocol for systems that are characterized b...
We consider the problem of providing optimal uncertainty quantification (UQ) – and hence rigorous ce...
We consider the problem of providing optimal uncertainty quantification (UQ) – and hence r...
This Part II of this series is concerned with establishing the feasibility of an extended data-on-de...
We propose a rigorous framework for Uncertainty Quantification (UQ) in which the UQ objectives and ...
This work is concerned with establishing the feasibility of a data-on-demand (DoD) uncertainty quant...
<p>Uncertainty quantification (UQ) is both an old and new concept. The current novelty lies in the ...
The field of uncertainty quantification (UQ) deals with physical systems described by an input-outpu...
This paper is dedicated to exploring the NASA Langley Challenge on Optimization under Uncertainty by...
We have recently proposed a rigorous framework for Uncertainty Quantification (UQ) in which UQ objec...
This paper is dedicated to exploring the NASA Langley Challenge on Optimization under Uncertainty by...
Uncertainty quantification in a safety analysis study can be conducted by considering the uncertain ...
The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models...
In the real world, a significant challenge faced in designing critical systems is the lack of availa...
The starting point in uncertainty quantification is a stochastic model, which is fitted to a technic...
We present an optimal uncertainty quantification (OUQ) protocol for systems that are characterized b...
We consider the problem of providing optimal uncertainty quantification (UQ) – and hence rigorous ce...
We consider the problem of providing optimal uncertainty quantification (UQ) – and hence r...
This Part II of this series is concerned with establishing the feasibility of an extended data-on-de...
We propose a rigorous framework for Uncertainty Quantification (UQ) in which the UQ objectives and ...
This work is concerned with establishing the feasibility of a data-on-demand (DoD) uncertainty quant...
<p>Uncertainty quantification (UQ) is both an old and new concept. The current novelty lies in the ...
The field of uncertainty quantification (UQ) deals with physical systems described by an input-outpu...
This paper is dedicated to exploring the NASA Langley Challenge on Optimization under Uncertainty by...
We have recently proposed a rigorous framework for Uncertainty Quantification (UQ) in which UQ objec...
This paper is dedicated to exploring the NASA Langley Challenge on Optimization under Uncertainty by...
Uncertainty quantification in a safety analysis study can be conducted by considering the uncertain ...
The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models...
In the real world, a significant challenge faced in designing critical systems is the lack of availa...
The starting point in uncertainty quantification is a stochastic model, which is fitted to a technic...