28 citations · 51 across the 13 of their papers we have counts for
14 papers
FlashNeRD: Performance-First Contact-Rich Neural Robot Dynamics
Mohammadmehdi Ataei, Mohammad Amin Nabian, Jie Xu +9
Compared with analytical physics, learned dynamics models promise robot simulation that is faster, inherently differentiable, and easily adaptable to real data. Neural Robot Dynami…
Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data
Mohammadmehdi Ataei, Farzaneh Askari, Kamal Rahimi Malekshan +1
Computer-Aided Design (CAD) models are defined by their construction history: a parametric recipe that encodes design intent. However, existing large-scale 3D datasets predominantl…
MechaFormer: Sequence Learning for Kinematic Mechanism Design Automation
Diana Bolanos, Mohammadmehdi Ataei, Pradeep Kumar Jayaraman
Designing mechanical mechanisms to trace specific paths is a classic yet notoriously difficult engineering problem, characterized by a vast and complex search space of discrete top…
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…
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 +1
Product recalls provide valuable insights into potential risks and hazards within the engineering design process, yet their full potential remains underutilized. In this study, we…
Accelerating Scientific Research Through a Multi-LLM Framework
Joaquin Ramirez-Medina, Mohammadmehdi Ataei, Alidad Amirfazli
The exponential growth of academic publications poses challenges for the research process, such as literature review and procedural planning. Large Language Models (LLMs) have emer…
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…
Reduced-order modeling of unsteady fluid flow using neural network ensembles
Rakesh Halder, Mohammadmehdi Ataei, Hesam Salehipour +2
The use of deep learning has become increasingly popular in reduced-order models (ROMs) to obtain low-dimensional representations of full-order models. Convolutional autoencoders (…
XLB: A differentiable massively parallel lattice Boltzmann library in Python
Mohammadmehdi Ataei, Hesam Salehipour
The lattice Boltzmann method (LBM) has emerged as a prominent technique for solving fluid dynamics problems due to its algorithmic potential for computational scalability. We intro…