15 citations · 28 across the 13 of their papers we have counts for
16 papers
CADBench: A Multimodal Benchmark for AI-Assisted CAD Program Generation
Anna C. Doris, Jacob Thomas Sony, Ghadi Nehme +3
Recovering editable CAD programs from images or 3D observations is central to AI-assisted design, but progress is difficult to measure because existing evaluations are fragmented a…
GIFT: Bootstrapping Image-to-CAD Program Synthesis via Geometric Feedback
Giorgio Giannone, Anna Clare Doris, Amin Heyrani Nobari +3
Generating executable CAD programs from images requires alignment between visual geometry and symbolic program representations, a capability that current methods fail to learn reli…
PGD-TO: A Scalable Alternative to MMA Using Projected Gradient Descent for Multi-Constraint Topology Optimization
Amin Heyrani Nobari, Faez Ahmed
Projected Gradient Descent (PGD) methods offer a simple and scalable approach to topology optimization (TO), yet they often struggle with nonlinear and multi-constraint problems du…
Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free Structural Topology Optimization
Amin Heyrani Nobari, Lyle Regenwetter, Cyril Picard +2
Structural topology optimization (TO) is central to engineering design but remains computationally intensive due to complex physics and hard constraints. Existing deep-learning met…
CAD-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code Generation
Anna C. Doris, Md Ferdous Alam, Amin Heyrani Nobari +1
Efficient creation of accurate and editable 3D CAD models is critical in engineering design, significantly impacting cost and time-to-market in product innovation. Current manual w…
Activation-Informed Merging of Large Language Models
Amin Heyrani Nobari, Kaveh Alim, Ali ArjomandBigdeli +3
Model merging, a method that combines the parameters and embeddings of multiple fine-tuned large language models (LLMs), offers a promising approach to enhance model performance ac…
Generative Optimization: A Perspective on AI-Enhanced Problem Solving in Engineering
Lyle Regenwetter, Cyril Picard, Amin Heyrani Nobari +2
The field of engineering is shaped by the tools and methods used to solve problems. Optimization is one such class of powerful, robust, and effective engineering tools proven over…
LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis
Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund +2
In this paper, we introduce LInK, a novel framework that integrates contrastive learning of performance and design space with optimization techniques for solving complex inverse pr…
BIKED++: A Multimodal Dataset of 1.4 Million Bicycle Image and Parametric CAD Designs
Lyle Regenwetter, Yazan Abu Obaideh, Amin Heyrani Nobari +1
This paper introduces a public dataset of 1.4 million procedurally-generated bicycle designs represented parametrically, as JSON files, and as rasterized images. The dataset is cre…
NITO: Neural Implicit Fields for Resolution-free Topology Optimization
Amin Heyrani Nobari, Giorgio Giannone, Lyle Regenwetter +1
Topology optimization is a critical task in engineering design, where the goal is to optimally distribute material in a given space for maximum performance. We introduce Neural Imp…
Conformal Predictions Enhanced Expert-guided Meshing with Graph Neural Networks
Amin Heyrani Nobari, Justin Rey, Suhas Kodali +2
Computational Fluid Dynamics (CFD) is widely used in different engineering fields, but accurate simulations are dependent upon proper meshing of the simulation domain. While highly…
LINKS: A dataset of a hundred million planar linkage mechanisms for data-driven kinematic design
Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund +1
In this paper, we introduce LINKS, a dataset of 100 million one degree of freedom planar linkage mechanisms and 1.1 billion coupler curves, which is more than 1000 times larger tha…
Deep Generative Models in Engineering Design: A Review
Lyle Regenwetter, Amin Heyrani Nobari, Faez Ahmed
Automated design synthesis has the potential to revolutionize the modern engineering design process and improve access to highly optimized and customized products across countless…
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…
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…
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…