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Lyle Regenwetter
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Proceedings Papers

Towards Domain-Adaptive, Resolution-Free 3D Topology Optimization With Neural Implicit Fields

Amin Heyrani Nobari, Lyle Regenwetter, Faez Ahmed
Proc. ASME. IDETC-CIE2024, Volume 3A: 50th Design Automation Conference (DAC), V03AT03A012, August 25–28, 2024
Publisher: American Society of Mechanical Engineers
Paper No: DETC2024-142135
https://doi.org/10.1115/DETC2024-142135
View Paper titled, Towards Domain-Adaptive, Resolution-Free 3D Topology Optimization With Neural Implicit Fields
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Topics: Resolution (Optics), Topology optimization, Topology, Shapes, Engineering design, Generative artificial intelligence, Graphics processing units, Innovation, Optimization, Trains
Journal Articles

Multi-Objective Counterfactuals for Design: A Model-Agnostic Counterfactual Search Method for Multi-Modal Design Modifications

Lyle Regenwetter, Yazan Abu Obaideh, Faez Ahmed
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. February 2025, 147(2): 021401.
Paper No: MD-23-1610
https://doi.org/10.1115/1.4065998
Published Online: August 28, 2024
View Article titled, Multi-Objective Counterfactuals for Design: A Model-Agnostic Counterfactual Search Method for Multi-Modal Design Modifications
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Topics: Design, Optimization
Proceedings Papers

Counterfactuals for Design: A Model-Agnostic Method for Design Recommendations

Lyle Regenwetter, Yazan Abu Obaideh, Faez Ahmed
Proc. ASME. IDETC-CIE2023, Volume 3A: 49th Design Automation Conference (DAC), V03AT03A008, August 20–23, 2023
Publisher: American Society of Mechanical Engineers
Paper No: DETC2023-117216
https://doi.org/10.1115/DETC2023-117216
View Paper titled, Counterfactuals for Design: A Model-Agnostic Method for Design Recommendations
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Topics: Design, Design automation, Weight (Mass), Bicycles, Collaboration, Optimization, Pareto optimization, Safety, Tradeoffs, Visualization
Proceedings Papers

Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Design

Lyle Regenwetter, Faez Ahmed
Proc. ASME. IDETC-CIE2022, Volume 3B: 48th Design Automation Conference (DAC), V03BT03A046, August 14–17, 2022
Publisher: American Society of Mechanical Engineers
Paper No: DETC2022-91344
https://doi.org/10.1115/DETC2022-91344
View Paper titled, Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Design
Open the PDF for Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Design in another window
Topics: Design, Algorithms, Bicycles, Machine learning
Journal Articles

Deep Generative Models in Engineering Design: A Review

Lyle Regenwetter, Amin Heyrani Nobari, Faez Ahmed
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Review Articles
J. Mech. Des. July 2022, 144(7): 071704.
Paper No: MD-21-1642
https://doi.org/10.1115/1.4053859
Published Online: March 18, 2022
View Article titled, Deep Generative Models in Engineering Design: A Review
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Topics: Design, Topology, Optimization, Shapes, Engineering design
Proceedings Papers

BIKED: A Dataset and Machine Learning Benchmarks for Data-Driven Bicycle Design

Lyle Regenwetter, Brent Curry, Faez Ahmed
Proc. ASME. IDETC-CIE2021, Volume 3A: 47th Design Automation Conference (DAC), V03AT03A019, August 17–19, 2021
Publisher: American Society of Mechanical Engineers
Paper No: DETC2021-71681
https://doi.org/10.1115/DETC2021-71681
View Paper titled, BIKED: A Dataset and Machine Learning Benchmarks for Data-Driven Bicycle Design
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Topics: Bicycles, Data-driven design, Design, Machine learning, Design methodology, Manufacturing
Journal Articles

BIKED: A Dataset for Computational Bicycle Design With Machine Learning Benchmarks

Lyle Regenwetter, Brent Curry, Faez Ahmed
Journal: Journal of Mechanical Design
Publisher: ASME
Article Type: Research Papers
J. Mech. Des. March 2022, 144(3): 031706.
Paper No: MD-21-1250
https://doi.org/10.1115/1.4052585
Published Online: October 29, 2021
View Article titled, BIKED: A Dataset for Computational Bicycle Design With Machine Learning Benchmarks
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Topics: Bicycles, Design