2 edition of Proceedings of Workshop on Model Uncertainty, Its Characterization and Quantification found in the catalog.
Proceedings of Workshop on Model Uncertainty, Its Characterization and Quantification
Workshop on Model Uncertainty, Its Characterization and Quantification (1993 Annapolis, Maryland)
Published
1995
by University Printing Services, University of Maryland in College Park, MD, U.S.A
.
Written in
Edition Notes
Includes bibliographical references.
| Other titles | Workshop on Model Uncertainty, Its Characterization and Quantification |
| Statement | edited by A. Mosleh ... [et al.]. |
| Series | International workshop series on advanced topics in reliability and risk analysis, |
| Contributions | Mosleh, A. |
| Classifications | |
|---|---|
| LC Classifications | TA169 .W66 1993 |
| The Physical Object | |
| Pagination | vi, 251 p. : |
| Number of Pages | 251 |
| ID Numbers | |
| Open Library | OL296703M |
| LC Control Number | 97202970 |
Vulnerability, Uncertainty, and Risk: Quantification, Mitigation, and Management, CDRM 9, contains peer-reviewed papers that build upon recent significant advances in the quantification, mitigation, and management of risk and uncertainty. These papers focus on decision making and multi-disciplinary developments to address the demands and. Workshop on Uncertainty Quantification and Data-Driven Modeling AT&T Executive Education and Conference Center, Austin, Texas March , Thursday, March 23 Registration; Room - Opening Remarks Session 1 – Roger Ghanem, University of Southern California Data-driven Sampling and Prediction on Manifolds.
The second outreach workshop “Frontiers of Uncertainty Quantification” (FrontUQ) organized by the GAMM Activity Group on Uncertainty Quantification will be held on September in Pavia, Italy with focus on Uncertainty Quantification in Subsurface Environments (https://frontuqorg). Subsurface environments host natural resources. Uncertainty quantification may appear daunting for practitioners due to its inherent complexity but can be intriguing and rewarding for anyone with mathematical ambitions and genuine concern for modeling quality. Uncertainty quantification is what remains to be done when too much credibility has been invested in deterministic analyses and unwarranted assumptions. Model .
3 Computational and Analytical Methods in Additive Manufacturing. The second sessions of the first two days of the workshop provided an overview of novel computational and analytical methods for fully characterizing process-structure-property relations in additive manufacturing (AM) processes for materials design, product design, part qualification, and discovery/innovation. Sequential approximation of uncertainty sets via parallelotopes.- A robust ellipsoidal-bound approach to direct adaptive control.- On line model uncertainty quantification: Hard upper bounds and convergence.- A mixed deterministic-probabilistic approach for quantifying uncertainty in Transfer Function Estimation.- Estimation for robust control
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Proceedings of the Workshop on Model Uncertainty. Its Characterization and QuantificationManufacturer: College Park. Model uncertainty, its characterization and quantification Proceedings of Workshop 1 in Advanced Topics in Risk and Reliability Analysis: Responsibility: edited by A.
Mosleh [and others] ; sponsored by U.S. Nuclear Regulatory Commission, EG & G Its Characterization and Quantification book, Inc., University of Maryland. Apostolakis, G.E. (), “A Commentary on Model Uncertainty”, Proceedings of the Workshop on Model Uncertainty: its Characterization and Quantification, published by Center for Reliability Engineering, University of Maryland, College Park, Maryland, USA.
Google ScholarCited by: 7. Data worth analysis is a field of research related to uncertainty quantification that has been used extensively in groundwater modeling [97] and more recently in TOUGH2 simulations [].
It is based on the premise that the worth of data increases in proportion to its ability to reduce the uncertainty of key model predictions [28]. The. International Workshop on Uncertainty Sensitivity and Parameter Estimation for Multimedia Environmental Modeling proceedings.
ECMWF | Reading | April Workshop description This joint ECMWF/WWRP workshop provided an opportunity for international experts to discuss the latest developments in diagnosing and characterising model error, and building schemes for simulating model uncertainty in assimilation and prediction systems.
Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 37 th IMAC, A Conference and Exposition on Structural Dynamics,the third volume of eight from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and.
Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 35 th IMAC, A Conference and Exposition on Structural Dynamics,the third volume of ten from the Conference brings together contributions to this important area of research and engineering.
The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and. Yong Bai, Wei-Liang Jin, in Marine Structural Design (Second Edition), Model Uncertainty.
Model uncertainty is uncertainty due to imperfections and idealizations made in physical model formulations for load and resistance, as well as in the choices of probability distribution types for the representation of uncertainties. With very few exceptions, it is often not possible to make highly.
Based on the uncertainty quantification with the Bayesian prediction and, subsequently, that of a design objective, some decision validation metrics are further developed to assess the confidence of using the Bayesian prediction model in making a specific design choice.
Model Uncertainty: Its Characterization and Quantification, A. Mosleh. Summary and Presentation Slides from Workshop on Risk Analysis for Autonomous Vehicles: Issues and Future Directions.
Modarres, Mohammad. Download Conference Slides Here. Proceedings of the Fifth International Workshop on Functional Modeling of Complex Technical Systems Model Uncertainty: Its Characterization and Quantification.
Uncertainty quantification (UQ) is the science of quantitative characterization and reduction of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known.
An example would be to predict the acceleration of a human body in a head-on crash with another car: even if we exactly knew. with sources of uncertainty. Some examples include • Geometrical uncertainty (Is the geometry exactly known?) • Initial and boundary data uncertainty (Are initial/boundary conditions precisely known?) • Structural uncertainty (Do the equations model the physics?) –.
Characterization capability evaluation is typically more complex with respect to current POD evaluations and thus requires engineering and statistical expertise in the model-building process to ensure all key effects and interactions are addressed. Justifying the statistical model choice with underlying assumptions is key.
On line model uncertainty quantification: Hard upper bounds and convergence.- A mixed deterministic-probabilistic approach for quantifying uncertainty in Transfer Function Estimation.- Estimation for robust control.- Non-vanishing model errors.- Accuracy confidence bands including the bias of model.
The mathematical and computational methods for estimating sensitivities in these problems also connect directly to the topics of Workshop III.
Uncertainty quantification has grown as a vital subject within the applied mathematics community, as witnessed by the emergence of a dedicated SIAM journal and regular SIAM conference. This study focuses on the development of efficient surrogate models by polynomial chaos expansion (PCE) for the prediction of the long-term extreme surge motion of a moored floati.
This paper deals with the identification in high dimensions of a polynomial chaos expansion of random vectors from a set of realizations.
Due to numerical and memory constraints, the usual polynomi. The SIAM Workshop on Combinatorial Scientific Computing (CSC20) was held February 11–13,in Seattle, Washington, USA. The CSC workshop series provides a top-tier forum for presenting original research on the design, implementation, application, and evaluation of combinatorial algorithms and data structures that arise from problems in computational science, computational.
TABLE OF CONTENTS Preface KEYNOTE PRESENTATIONS New Technology Frontiers on Commercial Aircrafts A New Look in Design of Intelligent Structures with SHM The Multidisciplinary Approach to SHM The Challenge of Long-Span Suspended Bridges Towards Damage and Structural Health Monitoring of Aerospace Composite Structures using Optical Fiber Sensors MONITORING OF.
The Bayesian UQ approach and subsequent resolution analysis are shown to be effective in assessing uncertainty in FE model updating.
Furthermore, it is demonstrated that the Bayesian FE model updating approach provides insight into the regularization of its often ill .Topics in Model Validation and Uncertainty Quantification, Volume 4, Proceedings of the 30 th IMAC, A Conference and Exposition on Structural Dynamics,the fourth volume of six from the Conference, brings together 19 contributions to this important area of research and engineering.
The collection presents early findings and case studies on fundamental and applied aspects of Structural Format: Paperback.6 Aleatory Uncertainty and Epistemic Uncertainty • Aleatory uncertainty is an inherent variation associated with the physical system or the environment – Also referred to as variability, irreducible uncertainty, and stochastic uncertainty, random uncertainty • Examples: – Variation in atmospheric conditions and angle of attack for inlet conditions – Variation in fatigue life of.
