Dr. Doostan's research team is focused on the development of novel theories and numerical tools to rigorously tackle several grand challenges associated with Uncertainty Quantification (UQ) and ...
Advances in computing hardware and algorithms have dramatically improved the ability to simulate complex processes computationally. Today's simulation capabilities offer the prospect of addressing ...
Uncertainty quantification (UQ) is essential for establishing the predictive accuracy of computational models for essentially all fields of science and engineering ...
Abstract: The increasing power of computing platforms and the recent advances in data science techniques have fostered the development of data-driven computational models of engineering systems with ...
Uncertainty quantification (UQ) is increasingly critical for modelling complex systems in which input parameters or environmental conditions vary unpredictably. Polynomial chaos methods offer a ...
Advances in computing hardware and algorithms have dramatically improved the ability to simulate complex processes computationally. Today's simulation capabilities offer the prospect of addressing ...
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