Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting [PDF]
iScience, 2022 Summary: This perspective discusses the importance of characterizing, quantifying, and accounting for various sources of uncertainties involved in different layers of hydrometeorological and hydrodynamic model simulations as well as their complex ...
Peyman Abbaszadeh +4 more
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Uncertainty quantification and reduction using Jacobian and Hessian information [PDF]
Design Science, 2021 Robust design methods have expanded from experimental techniques to include sampling methods, sensitivity analysis and probabilistic optimisation. Such methods typically require many evaluations.
Josefina Sánchez, Kevin Otto
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Underground Space, 2020 Subsurface stratigraphy is critical to the design, construction, and subsequent performance of geotechnical structures. However, in practice it is impossible to identify the stratigraphy of a subsurface geological domain with absolute certainty, due to ...
Xiangrong Wang
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Theoretical and Applied Mechanics LettersGenerative adversarial network (GAN) models are widely used in mechanical designs. The aim in the airfoil shape design is to obtain shapes that exhibits the required aerodynamic performance, and conditional GAN is used for that aim.
Kazuo Yonekura +2 more
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Disease progression modeling of Alzheimer's disease based on variational probability principal component analysis. [PDF]
PLoS ONEAlzheimer's disease (AD) is a neurodegenerative disorder and the leading cause of dementia. Early diagnosis and monitoring of disease progression are crucial for effective intervention.
Xin Xiong +4 more
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TOWARDS OVERCOMING THE CURSE OF DIMENSIONALITY IN PREDICTIVE MODELLING AND UNCERTAINTY QUANTIFICATION [PDF]
EPJ Web of Conferences, 2021 This invited presentation summarizes new methodologies developed by the author for performing high-order sensitivity analysis, uncertainty quantification and predictive modeling.
Cacuci Dan G.
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Uncertainty Quantification and Reduction Using Sensitivity Analysis and Hessian Derivatives [PDF]
Volume 3B: 47th Design Automation Conference (DAC), 2021 Abstract We study the use of Hessian interaction terms to quickly identify design variables that reduce variability of system performance. To start we quantify the uncertainty and compute the variance decomposition to determine noise variables that contribute most, all at an initial design.
Otto Kevin, Sánchez Josefina
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Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0) [PDF]
Geoscientific Model Development, 2020 Geological uncertainty quantification is critical to subsurface modeling and prediction, such as groundwater, oil or gas, and geothermal resources, and needs to be continuously updated with new data.
Z. Yin, S. Strebelle, J. Caers
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Uncertainty quantification and reduction in metal additive manufacturing [PDF]
npj Computational Materials, 2020 AbstractUncertainty quantification (UQ) in metal additive manufacturing (AM) has attracted tremendous interest in order to dramatically improve product reliability. Model-based UQ, which relies on the validity of a computational model, has been widely explored as a potential substitute for the time-consuming and expensive UQ solely based on experiments.
Zhuo Wang +8 more
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Mesh refinement for uncertainty quantification through model reduction [PDF]
Journal of Computational Physics, 2015 26 ...
Jing Li, Panos Stinis
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