367 citations · 598 across the 29 of their papers we have counts for
34 papers
2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
Jay Lee, Hanqi Su, Marco Macchi +50
The evolution of artificial intelligence (AI) and machine learning (ML) is reshaping smart manufacturing by providing new capabilities for efficiency, adaptability, and autonomy ac…
Machine learning assisted analysis of visible spectroscopy in pulsed-power-driven plasmas
Rishabh Datta, Faez Ahmed, Jack D Hare
We use machine learning models to predict ion density and electron temperature from visible emission spectra, in a high energy density pulsed-power-driven aluminum plasma, generate…
Conformal Predictions Enhanced Expert-guided Meshing with Graph Neural Networks
Amin Heyrani Nobari, Justin Rey, Suhas Kodali +2
Computational Fluid Dynamics (CFD) is widely used in different engineering fields, but accurate simulations are dependent upon proper meshing of the simulation domain. While highly…
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…
Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation
Giorgio Giannone, Akash Srivastava, Ole Winther +1
Generative models have had a profound impact on vision and language, paving the way for a new era of multimodal generative applications. While these successes have inspired researc…
Multi-modal Machine Learning for Vehicle Rating Predictions Using Image, Text, and Parametric Data
Hanqi Su, Binyang Song, Faez Ahmed
Accurate vehicle rating prediction can facilitate designing and configuring good vehicles. This prediction allows vehicle designers and manufacturers to optimize and improve their…
Ship-D: Ship Hull Dataset for Design Optimization using Machine Learning
Noah J. Bagazinski, Faez Ahmed
Machine learning has recently made significant strides in reducing design cycle time for complex products. Ship design, which currently involves years long cycles and small batch p…
DATED: Guidelines for Creating Synthetic Datasets for Engineering Design Applications
Cyril Picard, Jürg Schiffmann, Faez Ahmed
Exploiting the recent advancements in artificial intelligence, showcased by ChatGPT and DALL-E, in real-world applications necessitates vast, domain-specific, and publicly accessib…
ADVISE: AI-accelerated Design of Evidence Synthesis for Global Development
Kristen M. Edwards, Binyang Song, Jaron Porciello +3
When designing evidence-based policies and programs, decision-makers must distill key information from a vast and rapidly growing literature base. Identifying relevant literature f…
MCD: A Model-Agnostic Counterfactual Search Method For Multi-modal Design Modifications
Lyle Regenwetter, Yazan Abu Obaideh, Faez Ahmed
Designers may often ask themselves how to adjust their design concepts to achieve demanding functional goals. To answer such questions, designers must often consider counterfactual…
Diffusing the Optimal Topology: A Generative Optimization Approach
Giorgio Giannone, Faez Ahmed
Topology Optimization seeks to find the best design that satisfies a set of constraints while maximizing system performance. Traditional iterative optimization methods like SIMP ca…
Multi-modal Machine Learning in Engineering Design: A Review and Future Directions
Binyang Song, Rui Zhou, Faez Ahmed
In the rapidly advancing field of multi-modal machine learning (MMML), the convergence of multiple data modalities has the potential to reshape various applications. This paper pre…
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…
LINKS: A dataset of a hundred million planar linkage mechanisms for data-driven kinematic design
Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund +1
In this paper, we introduce LINKS, a dataset of 100 million one degree of freedom planar linkage mechanisms and 1.1 billion coupler curves, which is more than 1000 times larger tha…
Diffusion Models Beat GANs on Topology Optimization
François Mazé, Faez Ahmed
Structural topology optimization, which aims to find the optimal physical structure that maximizes mechanical performance, is vital in engineering design applications in aerospace,…
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…
PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design
Amin Heyrani Nobari, Wei Chen, Faez Ahmed
Engineering design tasks often require synthesizing new designs that meet desired performance requirements. The conventional design process, which requires iterative optimization a…
A Graph Neural Network Approach for Product Relationship Prediction
Faez Ahmed, Yaxin Cui, Yan Fu +1
Graph Neural Networks have revolutionized many machine learning tasks in recent years, ranging from drug discovery, recommendation systems, image classification, social network ana…
CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis
Amin Heyrani Nobari, Muhammad Fathy Rashad, Faez Ahmed
Modern machine learning techniques, such as deep neural networks, are transforming many disciplines ranging from image recognition to language understanding, by uncovering patterns…
Range-GAN: Range-Constrained Generative Adversarial Network for Conditioned Design Synthesis
Amin Heyrani Nobari, Wei Chen, Faez Ahmed
Typical engineering design tasks require the effort to modify designs iteratively until they meet certain constraints, i.e., performance or attribute requirements. Past work has pr…
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-…