BIKED: A Dataset and Machine Learning Benchmarks for Data-Driven Bicycle Design. - Lyle Regenwetter; Brent Curry; Faez Ahmed (CoRR, 2021)

Deep Generative Models in Engineering Design: A Review. - Lyle Regenwetter; Amin Heyrani Nobari; Faez Ahmed (CoRR, 2021)

FRAMED: Data-Driven Structural Performance Analysis of Community-Designed Bicycle Frames. - Lyle Regenwetter; Colin Weaver; Faez Ahmed (CoRR, 2022)

Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Design. - Lyle Regenwetter; Faez Ahmed (CoRR, 2022)

Towards Goal, Feasibility, and Diversity-Oriented Deep Generative Models in Design. - Lyle Regenwetter; Faez Ahmed (CoRR, 2022)

Counterfactuals for Design: A Model-Agnostic Method For Design Recommendations. - Lyle Regenwetter; Yazan Abu Obaideh; Faez Ahmed (CoRR, 2023)

Learning from Invalid Data: On Constraint Satisfaction in Generative Models. - Giorgio Giannone; Lyle Regenwetter; Akash Srivastava; Dan Gutfreund; Faez Ahmed (CoRR, 2023)

FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames. - Lyle Regenwetter; Colin Weaver; Faez Ahmed (Comput. Aided Des., 2023)

Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design. - Lyle Regenwetter; Akash Srivastava; Dan Gutfreund; Faez Ahmed (Comput. Aided Des., 2023)

From Positive to Negative: On the Role of Negative Data in Enhancing Generative Models for Engineering Constraint Satisfaction. - Lyle Regenwetter; Faez Ahmed (DDDAS/Infosymbiotics, 2024)

NITO: Neural Implicit Fields for Resolution-free Topology Optimization. - Amin Heyrani Nobari; Giorgio Giannone; Lyle Regenwetter; Faez Ahmed (CoRR, 2024)

BIKED++: A Multimodal Dataset of 1.4 Million Bicycle Image and Parametric CAD Designs. - Lyle Regenwetter; Yazan Abu Obaideh; Amin Heyrani Nobari; Faez Ahmed (CoRR, 2024)

Generative Optimization: A Perspective on AI-Enhanced Problem Solving in Engineering. - Cyril Picard; Lyle Regenwetter; Amin Heyrani Nobari; Akash Srivastava; Faez Ahmed (CoRR, 2024)

Constraining Generative Models for Engineering Design with Negative Data. - Lyle Regenwetter; Giorgio Giannone; Akash Srivastava; Dan Gutfreund; Faez Ahmed (Trans. Mach. Learn. Res., 2024)

NITO: Neural Implicit Fields for Resolution-free and Domain-Adaptable Topology Optimization. - Amin Heyrani Nobari; Lyle Regenwetter; Giorgio Giannone; Faez Ahmed (Trans. Mach. Learn. Res., 2025)

Bike-Bench: A Bicycle Design Benchmark for Generative Models with Objectives and Constraints. - Lyle Regenwetter; Yazan Abu Obaideh; Fabien Chiotti; Ioanna Lykourentzou; Faez Ahmed (CoRR, 2025)