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Journal Articles

A Bayesian Hierarchical Model for Extracting Individuals’ Theory-Based Causal Knowledge

Atharva Hans, Ashish M. Chaudhari, Ilias Bilionis, Jitesh H. Panchal
Journal: Journal of Computing and Information Science in Engineering
Publisher: ASME
Article Type: Research Papers
J. Comput. Inf. Sci. Eng. June 2023, 23(3): 031011.
Paper No: JCISE-22-1201
https://doi.org/10.1115/1.4055596
Published Online: December 12, 2022
View Article titled, A Bayesian Hierarchical Model for Extracting Individuals’ Theory-Based Causal Knowledge
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Topics: Modeling, Probability, Students, Testing, Design, Fatigue analysis, Uncertainty, Engineering education
Journal Articles

How Does Past Performance of Competitors Influence Designers’ Cognition, Behaviors, and Outcomes?

Murtuza N. Shergadwala, Jitesh H. Panchal, Ilias Bilionis
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. October 2022, 144(10): 101401.
Paper No: MD-20-1776
https://doi.org/10.1115/1.4054604
Published Online: June 30, 2022
View Article titled, How Does Past Performance of Competitors Influence Designers’ Cognition, Behaviors, and Outcomes?
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Topics: Design, Performance, Modeling, Decision making
Proceedings Papers

A Mixed-Method Analysis of Schedule and Cost Growth in Defense Acquisition Programs

Atharva Hans, Ashish M. Chaudhari, Ilias Bilionis, Jitesh H. Panchal
Proc. ASME. IDETC-CIE2021, Volume 3A: 47th Design Automation Conference (DAC), V03AT03A043, August 17–19, 2021
Publisher: American Society of Mechanical Engineers
Paper No: DETC2021-71517
https://doi.org/10.1115/DETC2021-71517
View Paper titled, A Mixed-Method Analysis of Schedule and Cost Growth in Defense Acquisition Programs
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Topics: Defense industry, Databases, Delays, Design, Governments, Modeling, Weapons
Proceedings Papers

Evaluating Heuristics in Engineering Design: A Reinforcement Learning Approach

Karim A. ElSayed, Ilias Bilionis, Jitesh H. Panchal
Proc. ASME. IDETC-CIE2021, Volume 3A: 47th Design Automation Conference (DAC), V03AT03A022, August 17–19, 2021
Publisher: American Society of Mechanical Engineers
Paper No: DETC2021-70425
https://doi.org/10.1115/DETC2021-70425
View Paper titled, Evaluating Heuristics in Engineering Design: A Reinforcement Learning Approach
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Topics: Engineering design, Reinforcement learning, Design, Simulation, Decision making, Engineering design processes, Performance, Preferences
Proceedings Papers

Quantifying Individuals’ Theory-Based Knowledge Using Probabilistic Causal Graphs: A Bayesian Hierarchical Approach

Atharva Hans, Ashish M. Chaudhari, Ilias Bilionis, Jitesh H. Panchal
Proc. ASME. IDETC-CIE2020, Volume 3: 17th International Conference on Design Education (DEC), V003T03A014, August 17–19, 2020
Publisher: American Society of Mechanical Engineers
Paper No: DETC2020-22613
https://doi.org/10.1115/DETC2020-22613
View Paper titled, Quantifying Individuals’ Theory-Based Knowledge Using Probabilistic Causal Graphs: A Bayesian Hierarchical Approach
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Topics: Decision making, Design, Engineering students, Probability, Scalars, Students
Journal Articles

Descriptive Models of Sequential Decisions in Engineering Design: An Experimental Study

Ashish M. Chaudhari, Ilias Bilionis, Jitesh H. Panchal
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. August 2020, 142(8): 081704.
Paper No: MD-19-1095
https://doi.org/10.1115/1.4045605
Published Online: February 14, 2020
View Article titled, Descriptive Models of Sequential Decisions in Engineering Design: An Experimental Study
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Topics: Design, Engineering design
Proceedings Papers

Similarity in Engineering Design: A Knowledge-Based Approach

Ashish M. Chaudhari, Ilias Bilionis, Jitesh H. Panchal
Proc. ASME. IDETC-CIE2019, Volume 7: 31st International Conference on Design Theory and Methodology, V007T06A045, August 18–21, 2019
Publisher: American Society of Mechanical Engineers
Paper No: DETC2019-98272
https://doi.org/10.1115/DETC2019-98272
View Paper titled, Similarity in Engineering Design: A Knowledge-Based Approach
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Topics: Design, Engineering design, Modeling, Design engineering, Education, Experimental design, Fatigue, Students
Proceedings Papers

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

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
Journal Articles

Bayesian Optimal Design of Experiments for Inferring the Statistical Expectation of Expensive Black-Box Functions

Piyush Pandita, Ilias Bilionis, Jitesh Panchal
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research-Article
J. Mech. Des. October 2019, 141(10): 101404.
Paper No: MD-18-1592
https://doi.org/10.1115/1.4043930
Published Online: July 10, 2019
View Article titled, Bayesian Optimal Design of Experiments for Inferring the Statistical Expectation of Expensive Black-Box Functions
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Topics: Design, Experimental design, Uncertainty, Steel, Algorithms, Wire, Optimization
Proceedings Papers

How Do Designers Choose Among Multiple Noisy Information Sources in Engineering Design Optimization? An Experimental Study

Ashish M. Chaudhari, Ilias Bilionis, Jitesh H. Panchal
Proc. ASME. IDETC-CIE2018, Volume 2A: 44th Design Automation Conference, V02AT03A021, August 26–29, 2018
Publisher: American Society of Mechanical Engineers
Paper No: DETC2018-85460
https://doi.org/10.1115/DETC2018-85460
View Paper titled, How Do Designers Choose Among Multiple Noisy Information Sources in Engineering Design Optimization? An Experimental Study
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Topics: Engineering design, Optimization, Uncertainty, Design, Decision making, Model development, Performance evaluation
Proceedings Papers

Understanding the Effect of Task Complexity and Problem-Solving Skills on the Design Performance of Agents in Systems Engineering

Salar Safarkhani, Ilias Bilionis, Jitesh H. Panchal
Proc. ASME. IDETC-CIE2018, Volume 2A: 44th Design Automation Conference, V02AT03A060, August 26–29, 2018
Publisher: American Society of Mechanical Engineers
Paper No: DETC2018-85941
https://doi.org/10.1115/DETC2018-85941
View Paper titled, Understanding the Effect of Task Complexity and Problem-Solving Skills on the Design Performance of Agents in Systems Engineering
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Topics: Design, Systems engineering, Complex systems, Performance, Systems engineers, Economics , Engineers, Noise (Sound), Optimization, Simulation
Proceedings Papers

Students As Sequential Decision-Makers: Quantifying the Impact of Problem Knowledge and Process Deviation on the Achievement of Their Design Problem Objective

Murtuza Shergadwala, Ilias Bilionis, Jitesh H. Panchal
Proc. ASME. IDETC-CIE2018, Volume 3: 20th International Conference on Advanced Vehicle Technologies; 15th International Conference on Design Education, V003T04A011, August 26–29, 2018
Publisher: American Society of Mechanical Engineers
Paper No: DETC2018-85537
https://doi.org/10.1115/DETC2018-85537
View Paper titled, Students As Sequential Decision-Makers: Quantifying the Impact of Problem Knowledge and Process Deviation on the Achievement of Their Design Problem Objective
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Topics: Design, Students, Decision making, Engineering design processes, Simulation, Statistical distributions, Uncertainty
Proceedings Papers

Deriving Information Acquisition Criteria for Sequentially Inferring the Expected Value of a Black-Box Function

Piyush Pandita, Ilias Bilionis, Jitesh Panchal
Proc. ASME. IDETC-CIE2018, Volume 2B: 44th Design Automation Conference, V02BT03A057, August 26–29, 2018
Publisher: American Society of Mechanical Engineers
Paper No: DETC2018-85893
https://doi.org/10.1115/DETC2018-85893
View Paper titled, Deriving Information Acquisition Criteria for Sequentially Inferring the Expected Value of a Black-Box Function
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Topics: Approximation, Computer simulation, Errors, Experimental design, Manufacturing, Steel, Wire
Journal Articles

Quantifying the Impact of Domain Knowledge and Problem Framing on Sequential Decisions in Engineering Design

Murtuza Shergadwala, Ilias Bilionis, Karthik N. Kannan, Jitesh H. Panchal
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research-Article
J. Mech. Des. October 2018, 140(10): 101402.
Paper No: MD-18-1187
https://doi.org/10.1115/1.4040548
Published Online: July 24, 2018
View Article titled, Quantifying the Impact of Domain Knowledge and Problem Framing on Sequential Decisions in Engineering Design
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Topics: Design, Framing (Construction), Structural frames, Modeling, Decision making, Testing, Performance, Optimization
Proceedings Papers

Extending Expected Improvement for High-Dimensional Stochastic Optimization of Expensive Black-Box Functions

Piyush Pandita, Ilias Bilionis, Jitesh Panchal
Proc. ASME. IDETC-CIE2016, Volume 2B: 42nd Design Automation Conference, V02BT03A060, August 21–24, 2016
Publisher: American Society of Mechanical Engineers
Paper No: DETC2016-60527
https://doi.org/10.1115/DETC2016-60527
View Paper titled, Extending Expected Improvement for High-Dimensional Stochastic Optimization of Expensive Black-Box Functions
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Topics: Optimization, Uncertainty, Approximation, Design, Chain, Engineering simulation, Filters, Measurement uncertainty, Particulate matter, Permeability
Journal Articles

Extending Expected Improvement for High-Dimensional Stochastic Optimization of Expensive Black-Box Functions

Piyush Pandita, Ilias Bilionis, Jitesh Panchal
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research-Article
J. Mech. Des. November 2016, 138(11): 111412.
Paper No: MD-16-1215
https://doi.org/10.1115/1.4034104
Published Online: September 12, 2016
View Article titled, Extending Expected Improvement for High-Dimensional Stochastic Optimization of Expensive Black-Box Functions
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Topics: Design, Optimization, Uncertainty, Simulation, Noise (Sound), Approximation
Journal Articles

Computationally Efficient Variational Approximations for Bayesian Inverse Problems

Panagiotis Tsilifis, Ilias Bilionis, Ioannis Katsounaros, Nicholas Zabaras
Journal: Journal of Verification, Validation and Uncertainty Quantification
Publisher: ASME
Article Type: Research-Article
J. Verif. Valid. Uncert. September 2016, 1(3): 031004.
Paper No: VVUQ-15-1041
https://doi.org/10.1115/1.4034102
Published Online: July 26, 2016
View Article titled, Computationally Efficient Variational Approximations for Bayesian Inverse Problems
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Topics: Approximation, Computation, Inverse problems, Noise (Sound), Optimization, Probability, Algorithms, Density, Chain, Dynamic systems