741 citations · 1.2k across the 17 of their papers we have counts for
21 papers
2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing
Jay Lee, Hanqi Su, Marco Macchi +50
The evolution of artificial intelligence (AI) and machine learning (ML) is reshaping smart manufacturing by providing new capabilities for efficiency, adaptability, and autonomy ac…
GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects
Nathan Hoffman, Cashen Diniz, Dehao Liu +3
Simulation-based approaches to microstructure generation can suffer from a variety of limitations, such as high memory usage, long computational times, and difficulties in generati…
Inverse design with conditional cascaded diffusion models
Milad Habibi, Mark Fuge
Adjoint-based design optimizations are usually computationally expensive and those costs scale with resolution. To address this, researchers have proposed machine learning approach…
Bayesian Inverse Problems with Conditional Sinkhorn Generative Adversarial Networks in Least Volume Latent Spaces
Qiuyi Chen, Panagiotis Tsilifis, Mark Fuge
Solving inverse problems in scientific and engineering fields has long been intriguing and holds great potential for many applications, yet most techniques still struggle to addres…
Least Volume Analysis
Qiuyi Chen, Cashen Diniz, Mark Fuge
This paper introduces Least Volume (LV)--a simple yet effective regularization method inspired by geometric intuition--that reduces the number of latent dimensions required by an a…
Assessing the trade-off between prediction accuracy and interpretability for topic modeling on energetic materials corpora
Monica Puerto, Mason Kellett, Rodanthi Nikopoulou +4
As the amount and variety of energetics research increases, machine aware topic identification is necessary to streamline future research pipelines. The makeup of an automatic topi…
IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular Structures
Jun Wang, Wei Wayne Chen, Daicong Da +2
Variable-density cellular structures can overcome connectivity and manufacturability issues of topologically optimized structures, particularly those represented as discrete densit…
Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks
Wei Chen, Kevin Chiu, Mark Fuge
Global optimization of aerodynamic shapes usually requires a large number of expensive computational fluid dynamics simulations because of the high dimensionality of the design spa…
Forming Diverse Teams from Sequentially Arriving People
Faez Ahmed, John Dickerson, Mark Fuge
Collaborative work often benefits from having teams or organizations with heterogeneous members. In this paper, we present a method to form such diverse teams from people arriving…
Adaptive Expansion Bayesian Optimization for Unbounded Global Optimization
Wei Chen, Mark Fuge
Bayesian optimization is normally performed within fixed variable bounds. In cases like hyperparameter tuning for machine learning algorithms, setting the variable bounds is not tr…
An Algorithm for Multi-Attribute Diverse Matching
Saba Ahmadi, Faez Ahmed, John P. Dickerson +2
Bipartite b-matching, where agents on one side of a market are matched to one or more agents or items on the other, is a classical model that is used in myriad application areas su…
Deep learning for molecular design - a review of the state of the art
Daniel C. Elton, Zois Boukouvalas, Mark D. Fuge +1
In the space of only a few years, deep generative modeling has revolutionized how we think of artificial creativity, yielding autonomous systems which produce original images, musi…
Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora
Daniel C. Elton, Dhruv Turakhia, Nischal Reddy +4
The number of scientific journal articles and reports being published about energetic materials every year is growing exponentially, and therefore extracting relevant information a…
Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning
Zois Boukouvalas, Daniel C. Elton, Peter W. Chung +1
Due to its high computational speed and accuracy compared to ab-initio quantum chemistry and forcefield modeling, the prediction of molecular properties using machine learning has…
BézierGAN: Automatic Generation of Smooth Curves from Interpretable Low-Dimensional Parameters
Wei Chen, Mark Fuge
Many real-world objects are designed by smooth curves, especially in the domain of aerospace and ship, where aerodynamic shapes (e.g., airfoils) and hydrodynamic shapes (e.g., hull…
Machine Learning of Energetic Material Properties
Brian C. Barnes, Daniel C. Elton, Zois Boukouvalas +4
In this work, we discuss use of machine learning techniques for rapid prediction of detonation properties including explosive energy, detonation velocity, and detonation pressure.…
Creative Exploration Using Topic Based Bisociative Networks
Faez Ahmed, Mark Fuge
Bisociative knowledge discovery is an approach that combines elements from two or more "incompatible" domains to generate creative solutions and insight. Inspired by Koestler's not…
Applying machine learning techniques to predict the properties of energetic materials
Daniel C. Elton, Zois Boukouvalas, Mark S. Butrico +2
We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a sma…
Ranking ideas for diversity and quality
Faez Ahmed, Mark Fuge
When selecting ideas or trying to find inspiration, designers often must sift through hundreds or thousands of ideas. This paper provides an algorithm to rank design ideas such tha…
Active Expansion Sampling for Learning Feasible Domains in an Unbounded Input Space
Wei Chen, Mark Fuge
Many engineering problems require identifying feasible domains under implicit constraints. One example is finding acceptable car body styling designs based on constraints like aest…
Diverse Weighted Bipartite b-Matching
Faez Ahmed, John P. Dickerson, Mark Fuge
Bipartite matching, where agents on one side of a market are matched to agents or items on the other, is a classical problem in computer science and economics, with widespread appl…