...). However, these surrogate models can introduce predictive errors, introducing epistemic uncertainty. The challenge arises when dealing with image input data, which is inherently high-dimensional, making it challenging to apply existing uncertainty quantification (UQ) techniques effectively. To address...

... growth uncertainty quantification Reusable spacecraft can be regarded as a feasible means to reduce the launch cost [ 1 ]. For a reusable spacecraft, its whole life cycle may suffer from various anomalies. Periodic maintenance and reusability evaluation are conducted to ensure flight reliability...

...-mail:  sankaran.mahadevan@vanderbilt.edu 13 07 2021 05 11 2021 07 01 2022 multiscale modeling model calibration surrogate model uncertainty quantification During the past few decades, the application of carbon fiber reinforced polymer (CFRP) composites has gained momentum...

...Sankaran Mahadevan; Paromita Nath; Zhen Hu This paper reviews the state of the art in applying uncertainty quantification (UQ) methods to additive manufacturing (AM). Physics-based as well as data-driven models are increasingly being developed and refined in order to support process optimization...

... laser powder-bed fusion modeling and simulation uncertainty quantification ontology Additive manufacturing (AM) produces parts by depositing powdered material layer-by-layer without the requirement of using specific tooling based on a three-dimensional (3D) model [ 1 ]. The laser powder-bed...

... of probabilistic modeling and uncertainty quantification on powder-bed fusion (PBF) additive manufacturing by focusing on the following three milieu: (a) accelerating the parameter development processes associated with laser powder bed fusion additive manufacturing process of metals, (b) quantifying uncertainty...

..., uncertainty quantification (UQ) analysis is performed on the silicon electrodeposition process to evaluate the impacts of various experimental operation parameters on the thickness variation of the coated silicon layer and to find the optimal experimental conditions. To mitigate the high experimental...

... important input parameters of the model. Third, systematic uncertainty quantification (UQ) scheme was employed to model the uncertainties of model input parameters based on their available—data-driven and physics-informed—information. Finally, the impact of the proposed UQ framework on the OWT structural...

... algorithms for optimization, uncertainty quantification with sampling, reliability, stochastic expansion methods, sensitivity analysis, and parameter study methods. The Latin Hypercube sampling (LHS) method is used for uncertainty quantification in the present work. The open-source toolkit provides flexible...

...Sifeng Bi; Michael Beer; Jingrui Zhang; Lechang Yang; Kui He The Bhattacharyya distance has been developed as a comprehensive uncertainty quantification metric by capturing multiple uncertainty sources from both numerical predictions and experimental measurements. This work pursues a further...