6 citations · 18 across the 12 of their papers we have counts for
10 papers · 1 filter
Engineering Regression Without Real-Data Training: Domain Adaptation for Tabular Foundation Models Using Multi-Dataset Embeddings
Lyle Regenwetter, Rosen Yu, Cyril Picard +1
Predictive modeling in engineering applications has long been dominated by bespoke models and small, siloed tabular datasets, limiting the applicability of large-scale learning app…
Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free Structural Topology Optimization
Amin Heyrani Nobari, Lyle Regenwetter, Cyril Picard +2
Structural topology optimization (TO) is central to engineering design but remains computationally intensive due to complex physics and hard constraints. Existing deep-learning met…
NITO: Neural Implicit Fields for Resolution-free Topology Optimization
Amin Heyrani Nobari, Giorgio Giannone, Lyle Regenwetter +1
Topology optimization is a critical task in engineering design, where the goal is to optimally distribute material in a given space for maximum performance. We introduce Neural Imp…
Constraining Generative Models for Engineering Design with Negative Data
Lyle Regenwetter, Giorgio Giannone, Akash Srivastava +2
Generative models have recently achieved remarkable success and widespread adoption in society, yet they often struggle to generate realistic and accurate outputs. This challenge e…
Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design
Lyle Regenwetter, Akash Srivastava, Dan Gutfreund +1
Deep generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, and Transformers, have shown great promise in a variety of…
Towards Goal, Feasibility, and Diversity-Oriented Deep Generative Models in Design
Lyle Regenwetter, Faez Ahmed
Deep Generative Machine Learning Models (DGMs) have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. DG…
Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Design
Lyle Regenwetter, Faez Ahmed
Deep Generative Machine Learning Models have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. While ear…
FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames
Lyle Regenwetter, Colin Weaver, Faez Ahmed
This paper demonstrates how Automated Machine Learning (AutoML) methods can be used as effective surrogate models in engineering design problems. To do so, we consider the challeng…
Deep Generative Models in Engineering Design: A Review
Lyle Regenwetter, Amin Heyrani Nobari, Faez Ahmed
Automated design synthesis has the potential to revolutionize the modern engineering design process and improve access to highly optimized and customized products across countless…
BIKED: A Dataset for Computational Bicycle Design with Machine Learning Benchmarks
Lyle Regenwetter, Brent Curry, Faez Ahmed
In this paper, we present "BIKED," a dataset comprised of 4500 individually designed bicycle models sourced from hundreds of designers. We expect BIKED to enable a variety of data-…