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Proceedings Papers

Additive Gaussian Process Regression for Metamodeling in High-Dimensional Problems

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Anh Tran, Kathryn Maupin, Ankush Kumar Mishra, Zhen Hu, Chao Hu
Proc. ASME. IDETC-CIE2023, Volume 2: 43rd Computers and Information in Engineering Conference (CIE), V002T02A012, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-116555
https://doi.org/10.1115/DETC2023-116555
View Paper titled, Additive Gaussian Process Regression for Metamodeling in High-Dimensional Problems
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Topics: Approximation, Design, Dimensions, Engineering systems and industry applications, Machine learning, Metamodeling, Optimization, Sensitivity analysis, Uncertainty quantification
Proceedings Papers

Physics-Informed Multi-Output Surrogate Modeling of Fusion Simulations

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Kathryn A. Maupin, Anh Tran, William E. Lewis, Michael E. Glinsky
Proc. ASME. IDETC-CIE2023, Volume 2: 43rd Computers and Information in Engineering Conference (CIE), V002T02A007, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-116989
https://doi.org/10.1115/DETC2023-116989
View Paper titled, Physics-Informed Multi-Output Surrogate Modeling of Fusion Simulations
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Topics: Metamodeling, Physics, Simulation, Uncertainty quantification, Calibration, Fuels, Magnetohydrodynamics, Compression, Computer simulation, Ignition
Proceedings Papers

Uncertainty Quantification on Mechanical Behavior of Corroded Plate With Statistical Shape Modeling

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Hao Wu, Parth Bansal, Zheng Liu, Yumeng Li, Pingfeng Wang
Proc. ASME. IDETC-CIE2023, Volume 3B: 49th Design Automation Conference (DAC), V03BT03A051, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-117050
https://doi.org/10.1115/DETC2023-117050
View Paper titled, Uncertainty Quantification on Mechanical Behavior of Corroded Plate With Statistical Shape Modeling
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Topics: Corrosion, Finite element analysis, Mechanical behavior, Modeling, Shapes, Uncertainty quantification
Proceedings Papers

Quantification Model Uncertainty of Label-Free Machine Learning for Multidisciplinary Systems Analysis

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Huiru Li, Jitesh H. Panchal, Xiaoping Du
Proc. ASME. IDETC-CIE2023, Volume 3B: 49th Design Automation Conference (DAC), V03BT03A045, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-112948
https://doi.org/10.1115/DETC2023-112948
View Paper titled, Quantification Model Uncertainty of Label-Free Machine Learning for Multidisciplinary Systems Analysis
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Topics: Artificial neural networks, Machine learning, Systems analysis, Uncertainty, Errors, Engineering disciplines, Combustion, Design, Engineering systems and industry applications, Uncertainty quantification
Proceedings Papers

Efficient Airfoil Geometric Uncertainty Quantification Using Neural Network Models and Sequential Sampling

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Pavankumar Koratikere, Leifur Leifsson
Proc. ASME. IDETC-CIE2023, Volume 3B: 49th Design Automation Conference (DAC), V03BT03A046, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-114954
https://doi.org/10.1115/DETC2023-114954
View Paper titled, Efficient Airfoil Geometric Uncertainty Quantification Using Neural Network Models and Sequential Sampling
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Topics: Airfoils, Neural network models, Uncertainty quantification, Algorithms, Computational fluid dynamics, Uncertainty, Drag (Fluid dynamics), Engineering simulation, Optimization, Simulation
Proceedings Papers

Uncertainty Quantification for Dissimilar Material Joints Under Corrosion Environment

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Parth Bansal, Zhuoyuan Zheng, Yumeng Li
Proc. ASME. IDETC-CIE2022, Volume 3A: 48th Design Automation Conference (DAC), V03AT03A039, August 14–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: DETC2022-89654
https://doi.org/10.1115/DETC2022-89654
View Paper titled, Uncertainty Quantification for Dissimilar Material Joints Under Corrosion Environment
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Topics: Corrosion, Machine learning, Physics, Resilience, Uncertainty quantification
Proceedings Papers

Uncertainty Quantification of Physics-Based Label-Free Deep Learning and Probabilistic Prediction of Extreme Events

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Huiru Li, Jianhua Yin, Xiaoping Du
Proc. ASME. IDETC-CIE2022, Volume 3B: 48th Design Automation Conference (DAC), V03BT03A001, August 14–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: DETC2022-88277
https://doi.org/10.1115/DETC2022-88277
View Paper titled, Uncertainty Quantification of Physics-Based Label-Free Deep Learning and Probabilistic Prediction of Extreme Events
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Topics: Physics, Reliability, Uncertainty, Uncertainty quantification, Artificial neural networks, Errors, Machine learning, Design, Linkages, Regression models
Proceedings Papers

Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty

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Wei (Wayne) Chen, Doksoo Lee, Oluwaseyi Balogun, Wei Chen
Proc. ASME. IDETC-CIE2022, Volume 3B: 48th Design Automation Conference (DAC), V03BT03A042, August 14–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: DETC2022-89707
https://doi.org/10.1115/DETC2022-89707
View Paper titled, Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty
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Topics: Design, Uncertainty, Manufacturing, Design under uncertainty, Optimization, Engineering design, Modeling, Reliability-based optimization, Shapes, Topology
Proceedings Papers

Multifidelity Uncertainty Quantification for Online Simulations of Automotive Propulsion Systems

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Hang Yang, Alex Gorodetsky, Yuji Fujii, Kon-Well Wang
Proc. ASME. IDETC-CIE2021, Volume 9: 17th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC), V009T09A043, August 17–19, 2021
Publisher: American Society of Mechanical Engineers
Paper No: DETC2021-67585
https://doi.org/10.1115/DETC2021-67585
View Paper titled, Multifidelity Uncertainty Quantification for Online Simulations of Automotive Propulsion Systems
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Topics: Engineering simulation, Propulsion systems, Simulation, Uncertainty quantification, Chaos, Polynomials, Uncertainty, Errors, Modeling, Nonlinear systems
Proceedings Papers

Uncertainty Quantification and Reduction Using Sensitivity Analysis and Hessian Derivatives

Open Access

Josefina Sánchez, Kevin Otto
Proc. ASME. IDETC-CIE2021, Volume 3B: 47th Design Automation Conference (DAC), V03BT03A013, August 17–19, 2021
Publisher: American Society of Mechanical Engineers
Paper No: DETC2021-71037
https://doi.org/10.1115/DETC2021-71037
View Paper titled, Uncertainty Quantification and Reduction Using Sensitivity Analysis and Hessian Derivatives
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Topics: Design, Sensitivity analysis, Uncertainty, Uncertainty quantification, Noise (Sound), Experimental design, Jacobian matrices, Modeling, Optimization, Simulation
Proceedings Papers

Quantification of Uncertainties Distributed in Network-Like Systems

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Zihan Wang, Hongyi Xu
Proc. ASME. IDETC-CIE2020, Volume 11A: 46th Design Automation Conference (DAC), V11AT11A048, August 17–19, 2020
Publisher: American Society of Mechanical Engineers
Paper No: DETC2020-22082
https://doi.org/10.1115/DETC2020-22082
View Paper titled, Quantification of Uncertainties Distributed in Network-Like Systems
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Topics: Simulation, Uncertainty, Uncertainty quantification, Additive manufacturing, Engineering systems and industry applications, Gaussian distribution, Heat conduction, Metamaterials, Stochastic processes
Proceedings Papers

Bayesian Optimization for Simulation-Based Design of Multi-Model Systems

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Siyu Tao, Anton van Beek, Daniel W. Apley, Wei Chen
Proc. ASME. IDETC-CIE2020, Volume 11B: 46th Design Automation Conference (DAC), V11BT11A022, August 17–19, 2020
Publisher: American Society of Mechanical Engineers
Paper No: DETC2020-22651
https://doi.org/10.1115/DETC2020-22651
View Paper titled, Bayesian Optimization for Simulation-Based Design of Multi-Model Systems
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Topics: Design, Optimization, Simulation, Algorithms, Couplings, Electronic packaging, Engineering design, Feedback, Modeling, Simulation models
Proceedings Papers

Uncertainty Quantification With Maximum Entropy Method for Fatigue Life Estimation

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S. M. Nielsen, H. A. Hougaard, O. Balling
Proc. ASME. IDETC-CIE2020, Volume 5: 24th Reliability, Stress Analysis, and Failure Prevention Conference (RSAFP), V005T05A008, August 17–19, 2020
Publisher: American Society of Mechanical Engineers
Paper No: DETC2020-22728
https://doi.org/10.1115/DETC2020-22728
View Paper titled, Uncertainty Quantification With Maximum Entropy Method for Fatigue Life Estimation
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Topics: Entropy, Fatigue life, Uncertainty quantification, Uncertainty, Ball bearings, Density, Fatigue, Finite element analysis, Maintenance, Probability
Proceedings Papers

Semi-Supervised Deep Learning for High-Dimensional Uncertainty Quantification

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Zequn Wang, Mingyang Li
Proc. ASME. IDETC-CIE2020, Volume 11A: 46th Design Automation Conference (DAC), V11AT11A001, August 17–19, 2020
Publisher: American Society of Mechanical Engineers
Paper No: DETC2020-22204
https://doi.org/10.1115/DETC2020-22204
View Paper titled, Semi-Supervised Deep Learning for High-Dimensional Uncertainty Quantification
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Topics: Dimensions, Evolutionary algorithms, Uncertainty quantification
Proceedings Papers

Deep Active Subspaces: A Scalable Method for High-Dimensional Uncertainty Propagation

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Rohit Tripathy, Ilias Bilionis
Proc. ASME. IDETC-CIE2019, Volume 1: 39th Computers and Information in Engineering Conference, V001T02A074, August 18–21, 2019
Publisher: American Society of Mechanical Engineers
Paper No: DETC2019-98099
https://doi.org/10.1115/DETC2019-98099
View Paper titled, Deep Active Subspaces: A Scalable Method for High-Dimensional Uncertainty Propagation
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Topics: Artificial neural networks, Chaos, Construction, Dimensions, Modeling, Polynomials, Response surface methodology, Space, Stochastic systems, Uncertainty
Proceedings Papers

Model Based Root Cause Analysis of Manufacturing Quality Problems Using Uncertainty Quantification and Sensitivity Analysis

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Kevin Otto, Josefina Sanchez Mosqueda
Proc. ASME. IDETC-CIE2019, Volume 1: 39th Computers and Information in Engineering Conference, V001T02A071, August 18–21, 2019
Publisher: American Society of Mechanical Engineers
Paper No: DETC2019-97766
https://doi.org/10.1115/DETC2019-97766
View Paper titled, Model Based Root Cause Analysis of Manufacturing Quality Problems Using Uncertainty Quantification and Sensitivity Analysis
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Topics: Manufacturing, Root cause analysis, Sensitivity analysis, Simulation, Uncertainty quantification
Proceedings Papers

A Strategy for Adaptive Sampling of Multi-Fidelity Gaussian Processes to Reduce Predictive Uncertainty

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Sayan Ghosh, Jesper Kristensen, Yiming Zhang, Waad Subber, Liping Wang
Proc. ASME. IDETC-CIE2019, Volume 2B: 45th Design Automation Conference, V02BT03A024, August 18–21, 2019
Publisher: American Society of Mechanical Engineers
Paper No: DETC2019-98418
https://doi.org/10.1115/DETC2019-98418
View Paper titled, A Strategy for Adaptive Sampling of Multi-Fidelity Gaussian Processes to Reduce Predictive Uncertainty
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Topics: Uncertainty, Design, Algorithms, Calibration, Computer software, Experimental design, Fluidized beds, Modeling, Optimization, Steady state
Proceedings Papers

Uncertainty Quantification of Reliability Analysis Under Surrogate Model Uncertainty Using Gaussian Process

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Sangjune Bae, Chanyoung Park, Nam H. Kim
Proc. ASME. IDETC-CIE2018, Volume 2B: 44th Design Automation Conference, V02BT03A055, August 26–29, 2018
Publisher: American Society of Mechanical Engineers
Paper No: DETC2018-85541
https://doi.org/10.1115/DETC2018-85541
View Paper titled, Uncertainty Quantification of Reliability Analysis Under Surrogate Model Uncertainty Using Gaussian Process
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Topics: Event history analysis, Uncertainty, Uncertainty quantification, Reliability, Chaos, Gaussian distribution, Simulation
Proceedings Papers

Assessing the Impact of Product Use Variation on Environmental Sustainability

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Melissa Tensa, Vincenzo Ferrero, Donovan Ross, Bryony DuPont
Proc. ASME. IDETC-CIE2018, Volume 4: 23rd Design for Manufacturing and the Life Cycle Conference; 12th International Conference on Micro- and Nanosystems, V004T05A031, August 26–29, 2018
Publisher: American Society of Mechanical Engineers
Paper No: DETC2018-85694
https://doi.org/10.1115/DETC2018-85694
View Paper titled, Assessing the Impact of Product Use Variation on Environmental Sustainability
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Topics: Sustainability, Uncertainty, Cycles, Decision making, Engineering design processes, Life cycle assessment, Uncertainty quantification
Proceedings Papers

Uncertainty Quantification and Validation of Lattice Structures Fabricated by Selective Laser Melting

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Recep M. Gorguluarslan, Seung-Kyum Choi, Hae-jin Choi
Proc. ASME. IDETC-CIE2017, Volume 1: 37th Computers and Information in Engineering Conference, V001T02A085, August 6–9, 2017
Publisher: American Society of Mechanical Engineers
Paper No: DETC2017-67438
https://doi.org/10.1115/DETC2017-67438
View Paper titled, Uncertainty Quantification and Validation of Lattice Structures Fabricated by Selective Laser Melting
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Topics: Lasers, Melting, Uncertainty quantification, Struts (Engineering), Uncertainty, Additive manufacturing, Simulation models, Accounting, Compression, Elasticity