activity
20172026
most citedDeep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems

367 citations · 386 across the 7 of their papers we have counts for

collaborators

13 papers

cs.AI20262 cited

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…

cs.LG2022

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…

cs.LG202115 cited

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…

cs.LG2021

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…

cs.LG20211 cited

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…

cs.LG20211 cited

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…