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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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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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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Bayesian deep reinforcement learning for uncertainty quantification and adaptive support optimization in deep foundation pit engineering [PDF]
Scientific ReportsThis study develops a novel framework integrating Bayesian inference with deep reinforcement learning for uncertainty quantification and adaptive support optimization in multi-physics coupled deep foundation pit systems.
Weiming Gu
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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 Jacobian and Hessian information
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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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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Geoscience Data Journal, 2022 The current handling of data in earth observation, modelling and prediction measures gives cause for critical consideration, since we all too often carelessly ignore data uncertainty.
Hendrik Paasche +4 more
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Quantification and Reduction of Uncertainty in Seismic Resilience Assessment for a Roadway Network
Infrastructures, 2023 The nation’s transportation systems are complex and are some of the highest valued and largest public assets in the United States. As a result of repeated natural hazards and their significant impact on transportation functionality and the socioeconomic ...
Vishnupriya Jonnalagadda +3 more
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Quantifying location error to define uncertainty in volcanic mass flow hazard simulations [PDF]
Natural Hazards and Earth System Sciences, 2021 The use of mass flow simulations in volcanic hazard zonation and mapping is often limited by model complexity (i.e. uncertainty in correct values of model parameters), a lack of model uncertainty quantification, and limited approaches to incorporate this
S. R. Mead, J. Procter, G. Kereszturi
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