1-5 of 5
Probability
Close
Save search
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Proceedings Papers

Probabilistic Deep Learning for Validation of Emergent Structures in Simulated Images

Bryan Kaiser, Kyle Hickmann
Proc. ASME. VVUQ2023, ASME 2023 Verification, Validation, and Uncertainty Quantification Symposium, V001T03A001, May 17–19, 2023
Publisher: American Society of Mechanical Engineers
Paper No: VVUQ2023-108722
https://doi.org/10.1115/VVUQ2023-108722
View Paper titled, Probabilistic Deep Learning for Validation of Emergent Structures in Simulated Images
Open the PDF for Probabilistic Deep Learning for Validation of Emergent Structures in Simulated Images in another window
Topics: Algorithms, Architecture, Artificial neural networks, Biomedicine, Complex systems, Density, Dimensions, Errors, Flow (Dynamics), Multiphase flow
Proceedings Papers

Bootstrapping and Jackknife Resampling to Improve Sparse-Sample UQ Methods for Tail Probability Estimation

Charles F. Jekel, Vicente Romero
Proc. ASME. VVS2019, ASME 2019 Verification and Validation Symposium, V001T06A003, May 15–17, 2019
Publisher: American Society of Mechanical Engineers
Paper No: VVS2019-5127
https://doi.org/10.1115/VVS2019-5127
View Paper titled, Bootstrapping and Jackknife Resampling to Improve Sparse-Sample UQ Methods for Tail Probability Estimation
Open the PDF for Bootstrapping and Jackknife Resampling to Improve Sparse-Sample UQ Methods for Tail Probability Estimation in another window
Topics: Probability, Uncertainty quantification, Reliability, Risk, Shapes, Uncertainty
Proceedings Papers

Bayesian Framework to Quantify Uncertainties in Piezoelectric Energy Harvesters

Patricio Peralta, Rafael O. Ruiz, Viviana Meruane
Proc. ASME. VVS2018, ASME 2018 Verification and Validation Symposium, V001T04A002, May 16–18, 2018
Publisher: American Society of Mechanical Engineers
Paper No: VVS2018-9318
https://doi.org/10.1115/VVS2018-9318
View Paper titled, Bayesian Framework to Quantify Uncertainties in Piezoelectric Energy Harvesters
Open the PDF for Bayesian Framework to Quantify Uncertainties in Piezoelectric Energy Harvesters in another window
Topics: Energy harvesting, Uncertainty, Design, Approximation, Density, Errors, Experimental characterization, Probability
Proceedings Papers

Models, Uncertainty, and the Sandia V&V Challenge Problem

George A. Hazelrigg, Jr., Georgia-Ann Klutke
Proc. ASME. VVS2018, ASME 2018 Verification and Validation Symposium, V001T03A001, May 16–18, 2018
Publisher: American Society of Mechanical Engineers
Paper No: VVS2018-9308
https://doi.org/10.1115/VVS2018-9308
View Paper titled, Models, Uncertainty, and the Sandia V&V Challenge Problem
Open the PDF for Models, Uncertainty, and the Sandia V&V Challenge Problem in another window
Topics: Errors, Mathematics, Probability, Uncertainty
Proceedings Papers

An Evaluation of Validation Metrics for Probabilistic Model Outputs

Paul Gardner, Charles Lord, Robert J. Barthorpe
Proc. ASME. VVS2018, ASME 2018 Verification and Validation Symposium, V001T06A001, May 16–18, 2018
Publisher: American Society of Mechanical Engineers
Paper No: VVS2018-9327
https://doi.org/10.1115/VVS2018-9327
View Paper titled, An Evaluation of Validation Metrics for Probabilistic Model Outputs
Open the PDF for An Evaluation of Validation Metrics for Probabilistic Model Outputs in another window
Topics: Computation, Engineering systems and industry applications, Modeling, Probability