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20232026
most citedXLB: A differentiable massively parallel lattice Boltzmann library in Python

28 citations · 51 across the 13 of their papers we have counts for

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6 papers · 1 filter

cs.LG2025★ 1 cited

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…

cs.HC2025

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…

cs.CL2025

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…

physics.app-ph2025

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…

cs.LG2025

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…

cs.LG2025

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…