18 citations · 25 across the 14 of their papers we have counts for
15 papers
Learning the Geometry of Admissible Hypotheses through Inductive Bias in Training Distributions
James Crowley, Faez Ahmed, Anton van Beek
Scientific discovery often requires reasoning over competing hypotheses that are consistent with experimental observations. For mixed-variable and combinatorial hypothesis spaces,…
BOCoDe: Engineering-Centered Benchmarking for Bayesian Optimization
Rosen Ting-Ying Yu, Christophe Hatterer, Advaith Narayanan +2
Bayesian optimization (BO) is a sample-efficient, surrogate-based approach to black-box optimization (BBO), but its evaluation remains dominated by synthetic functions and hyperpar…
Intrinsic Selection and Particle Resampling for Inference-Time Scaling Beyond Domain Verifiability
Giorgio Giannone, Mustafa Eyceoz, Shabana Baig +5
Inference-Time Scaling (ITS) has largely succeeded in verifiable domains like math and coding, where cheap verification enables scalable output selection. However, extending ITS to…
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…
Engineering Regression Without Real-Data Training: Domain Adaptation for Tabular Foundation Models Using Multi-Dataset Embeddings
Lyle Regenwetter, Rosen Yu, Cyril Picard +1
Predictive modeling in engineering applications has long been dominated by bespoke models and small, siloed tabular datasets, limiting the applicability of large-scale learning app…
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…
Discrete Noise Inversion for Next-scale Autoregressive Text-based Image Editing
Quan Dao, Xiaoxiao He, Ligong Han +6
Visual autoregressive models (VAR) have recently emerged as a promising class of generative models, achieving performance comparable to diffusion models in text-to-image generation…
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…
Fast and Accurate Bayesian Optimization with Pre-trained Transformers for Constrained Engineering Problems
Rosen, Yu, Cyril Picard +1
Bayesian Optimization (BO) is a foundational strategy in the field of engineering design optimization for efficiently handling black-box functions with many constraints and expensi…
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…
Fast and Accurate Zero-Training Classification for Tabular Engineering Data
Cyril Picard, Faez Ahmed
In engineering design, navigating complex decision-making landscapes demands a thorough exploration of the design, performance, and constraint spaces, often impeded by resource-int…
From Concept to Manufacturing: Evaluating Vision-Language Models for Engineering Design
Cyril Picard, Kristen M. Edwards, Anna C. Doris +4
Engineering design is undergoing a transformative shift with the advent of AI, marking a new era in how we approach product, system, and service planning. Large language models hav…