Daniele Grandi

According to our database, Daniele Grandi authored at least 19 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

A Domain Adaptation of Large Language Models for Classifying Mechanical Assembly Components.

Fatemeh Elhambakhsh, Daniele Grandi, Hyunwoong Ko

CoRR, May, 2025

On the effectiveness of Large Language Models in the mechanical design domain.

Daniele Grandi, Fabián Riquelme

CoRR, May, 2025

RECALL-MM: A Multimodal Dataset of Consumer Product Recalls for Risk Analysis using Computational Methods and Large Language Models.

Diana Bolanos, Mohammadmehdi Ataei, Daniele Grandi, Kosa Goucher-Lambert

CoRR, March, 2025

Do Large Language Models Produce Diverse Design Concepts? A Comparative Study with Human-Crowdsourced Solutions.

Kevin Ma, Daniele Grandi, Christopher McComb, Kosa Goucher-Lambert

J. Comput. Inf. Sci. Eng., 2025

Evaluating Large Language Models for Material Selection.

Daniele Grandi, Yash Patawari Jain, Allin Groom, Brandon Cramer, Christopher McComb

J. Comput. Inf. Sci. Eng., 2025

DesignQA: A Multimodal Benchmark for Evaluating Large Language Models' Understanding of Engineering Do cumentation.

Anna C. Doris, Daniele Grandi, Ryan Tomich, Md Ferdous Alam, Mohammadmehdi Ataei, Hyunmin Cheong, Faez Ahmed

J. Comput. Inf. Sci. Eng., 2025

Elicitron: A Large Language Model Agent-Based Simulation Framework for Design Requirements Elicitation.

Mohammadmehdi Ataei, Hyunmin Cheong, Daniele Grandi, Ye Wang, Nigel Morris, Alexander Tessier

J. Comput. Inf. Sci. Eng., 2025

2024

What's in a Name? Evaluating Assembly-Part Semantic Knowledge in Language Models Through User-Provided Names in Computer Aided Design Files.

Peter Meltzer, Joseph G. Lambourne, Daniele Grandi

J. Comput. Inf. Sci. Eng., January, 2024

HG-CAD: Hierarchical Graph Learning for Material Prediction and Recommendation in Computer-Aided Design.

Shijie Bian, Daniele Grandi, Tianyang Liu, Pradeep Kumar Jayaraman, Karl D. D. Willis, Elliot Sadler, Bodia Borijin, Thomas Lu, Richard Otis, Nhut Ho, Bingbing Li

J. Comput. Inf. Sci. Eng., January, 2024

MSEval: A Dataset for Material Selection in Conceptual Design to Evaluate Algorithmic Models.

Yash Patawari Jain, Daniele Grandi, Allin Groom, Brandon Cramer, Christopher McComb

CoRR, 2024

Exploring the Capabilities of Large Language Models for Generating Diverse Design Solutions.

Kevin Ma, Daniele Grandi, Christopher McComb, Kosa Goucher-Lambert

CoRR, 2024

Elicitron: An LLM Agent-Based Simulation Framework for Design Requirements Elicitation.

Mohammadmehdi Ataei, Hyunmin Cheong, Daniele Grandi, Ye Wang, Nigel Morris, Alexander Tessier

CoRR, 2024

DesignQA: A Multimodal Benchmark for Evaluating Large Language Models' Understanding of Engineering Documentation.

Anna C. Doris, Daniele Grandi, Ryan Tomich, Md Ferdous Alam, Hyunmin Cheong, Faez Ahmed

CoRR, 2024

2023

Conceptual Design Generation Using Large Language Models.

Kevin Ma, Daniele Grandi, Christopher McComb, Kosa Goucher-Lambert

CoRR, 2023

What's in a Name? Evaluating Assembly-Part Semantic Knowledge in Language Models through User-Provided Names in CAD Files.

Peter Meltzer, Joseph G. Lambourne, Daniele Grandi

CoRR, 2023

2022

Material Prediction for Design Automation Using Graph Representation Learning.

Shijie Bian, Daniele Grandi, Kaveh Hassani, Elliot Sadler, Bodia Borijin, Axel Fernandes, Andrew Wang, Thomas Lu, Richard Otis, Nhut Ho, Bingbing Li

CoRR, 2022

JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints.

Karl D. D. Willis, Pradeep Kumar Jayaraman, Hang Chu, Yunsheng Tian, Yifei Li, Daniele Grandi, Aditya Sanghi, Linh Tran, Joseph G. Lambourne, Armando Solar-Lezama, Wojciech Matusik

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

A3D: Studying Pretrained Representations with Programmable Datasets.

Ye Wang, Norman Mu, Daniele Grandi, Nicolas Savva, Jacob Steinhardt

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

2021

Classifying Component Function in Product Assemblies with Graph Neural Networks.

Vincenzo Ferrero, Kaveh Hassani, Daniele Grandi, Bryony DuPont

CoRR, 2021