1 citations · 1 across the 2 of their papers we have counts for
6 papers
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…
Generative Optimization: A Perspective on AI-Enhanced Problem Solving in Engineering
Lyle Regenwetter, Cyril Picard, Amin Heyrani Nobari +2
The field of engineering is shaped by the tools and methods used to solve problems. Optimization is one such class of powerful, robust, and effective engineering tools proven over…
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…
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…
BikeBench: A Bicycle Design Benchmark for Generative Models with Objectives and Constraints
Lyle Regenwetter, Yazan Abu Obaideh, Fabien Chiotti +2
We introduce BikeBench, an engineering design benchmark for evaluating generative models on problems with multiple real-world objectives and constraints. As generative AI's reach c…
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…