3 citations · 4 across the 7 of their papers we have counts for
7 papers
Transformer-Based Interfaces for Mechanical Assembly Design: A Gear Train Case Study
Mohammadmehdi Ataei, Hyunmin Cheong, Jiwon Jun +3
Generative artificial intelligence (AI), particularly transformer-based models, presents new opportunities for automating and augmenting engineering design workflows. However, effe…
e-SimFT: Alignment of Generative Models with Simulation Feedback for Pareto-Front Design Exploration
Hyunmin Cheong, Mohammadmehdi Ataei, Amir Hosein Khasahmadi +1
Deep generative models have recently shown success in solving complex engineering design problems where models predict solutions that address the design requirements specified as i…
mPOLICE: Provable Enforcement of Multi-Region Affine Constraints in Deep Neural Networks
Mohammadmehdi Ataei, Hyunmin Cheong, Adrian Butscher
Deep neural networks are increasingly used in safety-critical domains such as robotics and scientific modeling, where strict adherence to output constraints is essential. Methods l…
Physics Context Builders: A Modular Framework for Physical Reasoning in Vision-Language Models
Vahid Balazadeh, Mohammadmehdi Ataei, Hyunmin Cheong +2
Physical reasoning remains a significant challenge for Vision-Language Models (VLMs). This limitation arises from an inability to translate learned knowledge into predictions about…
Deep Generative Model for Mechanical System Configuration Design
Yasaman Etesam, Hyunmin Cheong, Mohammadmehdi Ataei +1
Generative AI has made remarkable progress in addressing various design challenges. One prominent area where generative AI could bring significant value is in engineering design. I…
Elicitron: An LLM Agent-Based Simulation Framework for Design Requirements Elicitation
Mohammadmehdi Ataei, Hyunmin Cheong, Daniele Grandi +3
Requirements elicitation, a critical, yet time-consuming and challenging step in product development, often fails to capture the full spectrum of user needs. This may lead to produ…
DesignQA: A Multimodal Benchmark for Evaluating Large Language Models' Understanding of Engineering Documentation
Anna C. Doris, Daniele Grandi, Ryan Tomich +4
This research introduces DesignQA, a novel benchmark aimed at evaluating the proficiency of multimodal large language models (MLLMs) in comprehending and applying engineering requi…