ccm/publications huggingface dataset:
Human versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design
Authors: Joshua T Gyory and Nicolas F Soria Zurita and Jay Martin and Corey Balon and Christopher McComb and Kenneth Kotovsky and Jonathan Cagan
Published in: Journal of Mechanical Design, 2022

Managing the design process of teams has been shown to considerably improve problem-solving behaviors and resulting final outcomes. Automating this activity presents significant opportunities in delivering interventions that dynamically adapt to the state of a team in order to reap the most impact. In this work, an artificial intelligence (AI) agent is created to manage the design process of engineering teams in real time, tracking features of teams’ actions and communications during a complex design and path-planning task in multidisciplinary teams. Teams are also placed under the guidance of human process managers for comparison. Regarding outcomes, teams perform equally as well under both types of management, with trends toward even superior performance from the AI-managed teams. The managers’ intervention strategies and team perceptions of those strategies are also explored, illuminating …
Using Autoencoded Voxel Patterns To Predict Part Mass, Required Support Material, and Build Time
Authors: Christian Murphy and Nicholas A Meisel and Timothy W Simpson and Christopher McComb
Published in: 29th Annual International Solid Freeform Fabrication Symposium, 2018

Additive Manufacturing (AM) allows designers to create intricate geometries that were once too complex or expensive to achieve through traditional manufacturing processes. Currently, designing parts using features specific to AM, commonly referred to as Design for Additive Manufacturing (DfAM), is restricted to experts in the field. As a result novices in industry may overlook potentially transformational design potential enabled by AM. This project aims to automate DfAM through deep learning making it accessible to a broader audience, and enabling designers of all skill levels to leverage unique AM geometries when creating new designs. To execute such an approach, a database of files was acquired from industry-sponsored AM challenges focused on lightweight design. These files were converted to a voxelized format, which provides more robust information for machine learning applications. Next, an autoencoder was constructed to a low-dimensional representation of the part designs. Finally, that autoencoder was used to construct a deep neural network capable of predicting various DfAM attributes. This work demonstrates a novel foray towards a more extensive DfAM support system that supports designers at all experience levels.
Utilizing Markov Chains to Understand Operation Sequencing in Design Tasks
Authors: Christopher McComb and Jonathan Cagan and Kenneth Kotovsky
Published in: Eight International Conference on Design Computing and Cognition, 2016

Design often involves searching for a final design solution by iteratively modifying and adjusting a current design. Through this process designers are able to improve the quality of the current design and also learn what patterns of operations are most likely to lead to the quickest future improvements. Prior work in psychology has shown that humans can be adept at learning how to apply short sequences of operations for maximum effect while solving a problem. This work explores the sequencing of operations specifically within the domain of engineering design by examining the results of a human study in which participants designed trusses. A statistical analysis of the data from that study uses Markov Chains to show with high confidence that meaningful operation sequences exist. This work also uses an agent-based modeling framework in conjunction with Markov Chain concepts to simulate the …
Expanding the Generative Power of Large Language Models (LLMs) for Design through Formal Design Grammars and Languages
Authors: Kristina Shea and Stankovic Tino and Akash Agrawal and Jonathan Cagan and Christopher McComb
Published in: Journal of Computing and Information Science in Engineering, 2025

Research in design grammars has been underway for over 50 years and has demonstrated great generative power for a wide range of design and engineering domains. A key limitation, though, is the lack of support for designers to develop and computationally implement formal design grammars. We explore the potential of large language models (LLMs) to act as a collaborative grammar development partner that works with human designers and provides guidance during grammar development, as well as serving as a grammar interpreter that converts natural language descriptions of design grammars into executable python code. Methods for both interpreting previously known design grammars as well as interactively and collaboratively developing a new design grammar that is not known a priori are proposed. Three case studies, namely a truss design grammar, a half-hexagon shape grammar, and a …
Adaptive Learning of Design Policies Over Nonhierarchical Multi-Fidelity Models Guided by Policy Alignment
Authors: Akash Agrawal and Christopher McComb
Published in: Journal of Mechanical Design 148 (1), 011703, 2026

Multifidelity reinforcement learning (RL) frameworks significantly enhance the efficiency of engineering design by leveraging analysis models with varying levels of accuracy and computational costs. The prevailing methodologies, characterized by transfer learning, human-inspired strategies, control variate techniques, and adaptive sampling, predominantly depend on a structured hierarchy of models. However, this reliance on a model hierarchy overlooks the heterogeneous error distributions of models across the design space, extending beyond mere fidelity levels. This work proposes adaptively learned policy with heterogeneous analyses (ALPHA), a novel multifidelity RL framework to efficiently learn a high-fidelity policy by adaptively leveraging an arbitrary set of nonhierarchical, heterogeneous, low-fidelity models alongside a high-fidelity model. Specifically, low-fidelity policies and their experience data …
CARA: The Corporate AI Readiness Assessment
Authors: Giovanni Natasha and Leyi Zhang and Jonathan Cagan and Christopher McComb
Published in: International Design Engineering Technical Conferences and Computers and …, 2025

Artificial Intelligence (AI) adoption is rapidly transforming industries, yet many organizations struggle to assess their readiness for AI integration. This paper introduces the Corporate AI Readiness Assessment (CARA), a tool designed to evaluate an organization’s preparedness for AI implementation across three critical dimensions: organizational readiness, workforce readiness, and technology readiness. CARA provides a structured framework that enables companies to identify strengths, gaps, and necessary actions to support AI-driven initiatives. While applicable across various industries, this assessment is particularly valuable for Small and Medium enterprises (SMEs) in manufacturing and engineering design, where resource constraints often present challenges in AI adoption. The assessment is structured as a survey, offering a scoring system that classifies organizations into four readiness categories …
Neural Network Surrogate Modeling for Stochastic Finite Element Method Using Three-Dimensional Graph Representations: A Comparative Study
Authors: Jessica Ezemba and Christopher McComb and Conrad Tucker
Published in: Journal of Mechanical Design 148 (1), 011704, 2026

Modern engineering design increasingly relies on probabilistic simulation to account for uncertainties in geometry and loading conditions. The stochastic finite element method (SFEM) has become standard as a way to address this need, using thousands of deterministic FEM evaluations to estimate the uncertainty. However, this creates a prohibitively high computational cost that can inhibit efficient design exploration. Neural network (NN) surrogate models offer a promising alternative, shifting computation to a one-time upfront training cost while enabling near-instantaneous subsequent evaluations for iterative design tasks. However, effective NN surrogates for SFEM must learn to directly predict distributions that traditionally emerge from iterative sampling and aggregation of system responses across varying parameter spaces. Although previous research has explored various NN architectures for physical …
Forging EMPIRE: A data-driven agent-based model for scenario-based generalizability in spatio-temporal human behavior modeling
Authors: Maral Doctorarastoo and Katherine A Flanigan and Mario Bergés and Christopher McComb
Published in: Computers, Environment and Urban Systems 126, 102418, 2026

People spend the majority of their lives within built environments, whose design can profoundly influence human- and community-centered outcomes such as social capital formation, access to opportunity, public health, and resilience to disruption. Just as the built environment shapes human behavior and well-being, its design, operation, and performance can be substantially improved by better understanding how people actually use and experience space. Yet both of these goals — enhancing human benefits from built environments and improving system performance through human-aware design — are constrained by a fundamental limitation: existing computational models oversimplify human agents, equipping them with static or assumed behavioral rules that fail to reflect the dynamic, adaptive, and context-sensitive nature of real-world behavior. These simplifications undermine generalizability, limiting the …
VizArQA: A Foundation for Visual Question Answering in Architectural Simulation
Authors: Manasi Dushyant Mehta and Jessica Ezemba and Conrad Tucker and Christopher McComb
Published in: Journal of Engineering for Sustainable Buildings and Cities, 2026

Environmental simulations are critical in early-stage design, offering insights into factors like solar radiation, airflow, and climate responsiveness. However, interpreting these simulation outputs is time consuming and requires expert reasoning, which can slow down design workflows. Vision-language models (VLMs) offer potential for automated interpretation support, yet their reliability on architectural simulation outputs remains unexplored, particularly across different computational scales. This study introduces VizArQA, a benchmark for evaluating VLM performance on architectural environmental simulation interpretation. VizArQA comprises 121 questions across five standard simulation types (radiation roses, wind roses, illuminance diagrams, solar insolation, and computational fluid dynamics plots), with both binary and multiple-choice formats that assess visual comprehension and design-relevant …
Developing an AI-assisted Approach for Surveying CAAD Research: An overview of three decades of research at CAADRIA
Authors: Yuval Kahlon and Christopher McComb and Haruyuki Fujii
Published in: 30th International Conference on Computer-Aided Architectural Design …, 2025

As Design Computing becomes central to design practice, publications in the field continue to increase. In this situation, researchers need to find new ways to effectively utilize the abundance of information available. Naturally, computational tools emerge as a promising solution for analysing and gaining insight into such large amounts of data. Capitalizing on this opportunity, we establish a pipeline for surveying existing works in the important subfield of Computer-aided Architectural Design (CAAD) research, focusing on the proceedings of CAADRIA, as a case study (1996-2024). The method employed in this study consists of a combination of human-annotation of existing papers with two computational techniques:(1) constructing and testing classifiers via automated machine learning and,(2) analysis and visualization of keyword co-occurrence. Our results show that the proposed pipeline can help us gain insight into trends in CAAD Research, as well as aid in the automated classification of papers into several important classes, thereby contributing to the broader endeavour of mapping Design Computing Research.
Comparative Evaluation of Neural Network Architectures for Generalizable Human Spatial Preference Prediction in Unseen Built Environments
Authors: Maral Doctorarastoo and Katherine A Flanigan and Mario Berges and Christopher McComb
Published in: 15th International Workshop on Structural Health Monitoring, 2025

The capacity to predict human spatial preferences within built environments is instrumental for developing Cyber-Physical-Social Infrastructure Systems (CPSIS). A significant challenge in this domain is the generalizability of preference models, particularly their efficacy in predicting preferences within environmental configurations not encountered during training. While deep learning models have shown promise in learning complex spatial and contextual dependencies, it remains unclear which neural network architectures are most effective at generalizing to unseen layouts. To address this, we conduct a comparative study of Graph Neural Networks, Convolutional Neural Networks, and standard feedforward Neural Networks using synthetic data generated from a simplified and synthetic pocket park environment. Beginning with this illustrative case study, allows for controlled analysis of each model's ability to transfer learned preference patterns to unseen spatial scenarios. The models are evaluated based on their capacity to predict preferences influenced by heterogeneous physical, environmental, and social features. Generalizability score is calculated using the area under the precision-recall curve for the seen and unseen layouts. This generalizability score is appropriate for imbalanced data, providing insights into the suitability of each neural network architecture for preference-aware human behavior modeling in unseen built environments.
Special Issue on Generative Artificial Intelligence for Design, Manufacturing Processes, and Materials Systems: Part I
Authors: Wei “Wayne” Chen and Vinayak Raman Krishnamurthy and Yanglong Lu and Jianxi Luo and Christopher McComb and Sandipp Krishnan Ravi and Zhenghui Sha
Published in: Journal of Computing and Information Science in Engineering 26 (7), 070301, 2026

Generative artificial intelligence (AI) refers to the domain of AI systems designed to generate new information and artifacts by sampling from complex distributions captured from the data they were trained on. Encompassing techniques such as generative adversarial networks, variational autoencoders, diffusion models, large language models (LLMs), and vision-language models (VLMs), generative AI is fundamentally revolutionizing the engineering domain. Moving beyond traditional descriptive and predictive modeling, these technologies have demonstrated an exceptional capacity to enable the end-to-end creation of design solutions, optimize high-dimensional complex systems, and synthesize deep, multimodal insights into engineering problems. By leveraging multimodal data, such as textual, visual, and physical signals, generative AI can be used to address critical tasks ranging from conceptual design …
Greater than the sum of our parts: the new division of labor in design with AI
Authors: Jessica Menold and Christopher McComb and Conrad Tucker and Panos Papalambros
Published in: PNAS Nexus, 2026

Design is how humans change the world, and in the age of AI it is increasingly a joint activity between humans and machines. This perspective argues that AI does not simply add new tools to the designer’s repertoire; it has the capability to reorganize the division of labor in design and, in doing so, reshape what it means to be a designer. We distinguish between representative technologies, which model and derisk complex systems (eg digital twins, immersive simulations), and operative technologies, which act directly within design processes (eg generative and agentic systems that propose, evaluate, and select alternatives). Viewed through the Five-Cycle model of design, we argue that these technologies widen inputs, accelerate exploration, and tighten feedback across problem definition, conceptual and embodiment design, and value proposition. In our model, representative tools derisk what to believe …
Supervising Ralph Wiggum: Exploring a Metacognitive Co-Regulation Agentic AI Loop for Engineering Design
Authors: Zeda Xu and Nikolas Martelaro and Christopher McComb
Published in: International Design Engineering Technical Conferences and Computers and …, 2026

The engineering design research community has studied agentic AI systems that use Large Language Model (LLM) agents to automate the engineering design process. However, these systems are prone to some of the same pathologies that plague humans. Just as human designers, LLM design agents can fixate on existing paradigms and fail to explore alternatives when solving design challenges, potentially leading to suboptimal solutions. In this work, we propose (1) a novel Self-Regulation Loop (SRL), in which the Design Agent self-regulates and explicitly monitors its own metacognition, and (2) a novel Co-Regulation Design Agentic Loop (CRDAL), in which a Metacognitive Co-Regulation Agent assists the Design Agent in metacognition to mitigate design fixation, thereby improving system performance for engineering design tasks. In the battery pack design problem examined here, we found that the novel SRL and CRDAL systems generate designs with better performance, without significantly increasing the computational cost, compared to a plain Ralph Wiggum Loop (RWL) Further, the novel CRDAL generates designs with significantly better performance than SRL. Also, we found that the CRDAL system navigated through the latent design space more effectively than both SRL and RWL. The proposed system architectures and findings of this work provide practical implications for future development of agentic AI systems for engineering design.
Scalable super-resolution of flow conveyance systems through adaptive domain decomposition
Authors: Wenzhuo Xu and Akibi Archer and Mike McCarrell and Scott Hesser and Noelia Grande Gutierrez and Christopher McComb
Published in: Journal of Computing and Information Science in Engineering, 2026

Modern engineering design requires high-fidelity simulations, which can impose an enormous computational burden and slow the speed of design iteration. Data-driven up-sampling methods like physics-informed neural networks (PINNs) help reduce the computational resources required. However, machine learning model capacity and hardware limitations still pose challenges when evaluating large engineering simulations with complex physics dynamics. Recently, methods have been proposed to enforce the principle of locality in physical systems to neural network layers, allowing for concurrent inference on smaller subdomains with improved efficiency and accuracy. Based on such an idea, we extend the theory of domain decomposition to complex three-dimensional geometries using graph neural networks (GNNs). We developed a graph decomposition method to improve the training and inference …
'Interaction Twin in the Middle': A distributed digital twin architecture to model team interactions and dynamics for deep space missions
Authors: Patrick Pischulti and Min Young Hwang and Christopher McComb and Katya Arquilla
Published in: Frontiers in Aerospace Engineering, 2026

NASA’s Moon to Mars campaign emphasizes the need for crews and habitat systems to operate with increasing autonomy as communication delays with Earth grow beyond 5 minutes. The digital twin framework has emerged as a promising solution to monitor, diagnose, predict, and optimize space systems, but prior aerospace applications have largely centered on system autonomy rather than crew autonomy. As a result, current approaches under-represent the interaction dynamics needed by mission control to continuously evolve procedure and accomplish mission objectives. This work introduces an Interaction Digital Twin (IDT) framework that twins the interactions between humans and systems rather than focusing only on individual entities. Built on a distributed digital twin architecture with bidirectional information flow, the framework integrates three complementary types of twins: Digital Twins for habitat systems, Human Digital Twins (HDTs) for individual crew members, and Interaction Digital Twins that capture emergent phenomena such as team cohesion, trust calibration, coordination, and adaptive autonomy. Twinning the interactions moves aspects of command and control on-board, giving crew mission-control-like capabilities even during periods of communication delay. We apply the framework to an Artemis Phase II mission scenario, demonstrating how interaction-level twinning extends system-level modeling to support cognitive workload management, information sharing, and human–autonomy teaming. By elevating interactions to first-class, inference-capable elements within the digital twin architecture, this framework bridges the …
Aligning Agents with Experts in Material Selection: Prompting, Size, and Reasoning
Authors: Megan Ying and Daniele Grandi and Allin Groom and Christopher McComb
Published in: Journal of Computing and Information Science in Engineering, 2026

Material selection is fundamental to the design process, as it significantly affects the cost, performance, appearance, manufacturability, and sustainability of a product. It is also a complex, open-ended challenge that forces designers to continuously adapt to new information, balance diverse stakeholder demands, weigh trade-offs, and navigate uncertainties to achieve the optimal outcome. Previous studies have explored the potential of large language models (LLMs) to assist in the material selection process, with findings suggesting that LLMs have the potential to provide valuable support. However, discrepancies between LLM outputs and expert recommendations indicate the need for further investigation. Recently, agentic artificial intelligence (AI) methods have been developed to address the limitations of standalone LLMs. AI agents integrate LLMs with external tools, allowing them to retrieve and analyze …
Evaluating Few-Shot Temporal Reasoning of LLMs for Human Activity Prediction in Smart Environments
Authors: Maral Doctorarastoo and Katherine A. Flanigan and Mario Berges and Christopher McComb
Published in: 2026 IEEE Conference on Cognitive and Computational Aspects of Situation …, 2026

Anticipating human activities and their durations is essential in applications such as smart-home automation, simulation-based architectural and urban design, activity-based transportation system simulation, and human-robot collaboration, where adaptive systems must respond to human activities. Existing data-driven agent-based models-from rule-based to deep learning-struggle in low-data environments, limiting their practicality. This paper investigates whether large language models, pre-trained on broad human knowledge, can fill this gap by reasoning about everyday activities from compact contextual cues. We adopt a retrieval-augmented prompting strategy that integrates four sources of context-temporal, spatial, behavioral history, and persona-and evaluate it on the CASAS Aruba smart-home dataset. The evaluation spans two complementary tasks: next-activity prediction with duration estimation, and multi …