ccm/publications huggingface dataset:
Characterizing Sequential Patterns of Human Behavior in Advanced Manufacturing
Authors: Melinda Mudzurandende and Katherine Flanigan and Christopher McComb
Published in: Journal of Computing and Information Science in Engineering, 2026

Understanding sequential human behavior in manufacturing is essential for improving productivity, safety, and human–machine collaboration. However, little is known about how the temporal structure of these behaviors varies across time scales or how such patterns can be systematically modeled to support adaptive, human-centered manufacturing systems. This article investigates the temporal structure of human interactions involved with wire arc additive manufacturing (WAAM), which serves as a representative context for advanced manufacturing environments. Using a large-scale dataset of annotated human activity in an advanced WAAM environment, we apply generative sequential models to systematically analyze how behavioral predictability and structure vary with sampling frequencies. This analysis reveals that human behavior in WAAM is highly sequential and temporally persistent, with a small …
OpenSeeSimE: A Large-Scale Benchmark to Assess Vision-Language Model Question Answering Capabilities in Engineering Simulations
Authors: Jessica Ezemba and Jason Pohl and Conrad Tucker and Christopher McComb

Engineering simulation interpretation is a major bottleneck in design cycles, requiring expensive domain expertise to validate complex outputs and ensure safety and performance. While modern large language models (LLMs) may assist in interpretation, they face fundamental scalability limitations, as even modest simulations exceed the context windows of best-in-class LLMs. Vision-language models (VLMs), having demonstrated success across technical visual reasoning domains from medical imaging to materials characterization, represent a promising alternative for processing simulation visualizations as compressed representations. However, their effectiveness for engineering simulation interpretation remains unknown, constrained by the absence of large-scale evaluation frameworks and prohibitive expert annotation costs. We introduce OpenSeeSimE, a large-scale benchmark consisting of 200,000+ question-answer pairs across 10,000 parametrically-varied simulations. This 850× scale increase, enables statistically robust evaluation across diverse simulation configurations and question types. Evaluation of ten state-of-the-art VLMs reveals a fundamental discovery: models that demonstrate strong performance on general visual reasoning benchmarks perform at random chance levels (29-47%) on engineering simulations with negligible effect sizes, establishing critical baselines for domain-specific model development
Graph-Based Domain Decomposition for Scalable Cardiovascular Flow Super-Resolution
Authors: Wenzhuo Xu and Christopher McComb and Noelia Grande Gutierrez
Published in: 78th Annual Meeting of the Division of Fluid Dynamics, 2025

Cardiovascular flow simulations require high-fidelity computational fluid dynamics modeling to accurately capture complex hemodynamic phenomena. However, this can impose an enormous computational burden, hindering computational translations of computational tools. Data-driven up-sampling methods like physics-informed neural networks (PINNs) help reduce the computational resources required. However, the capacity of machine learning models and hardware limitations still pose challenges when evaluating large-scale cardiovascular simulations with complex blood flow dynamics. Recently, we proposed a method that applies 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 vascular …
Ceci N'est Pas un Drone: Investigating the Impact of Design Representation on Design Decision Making When Using GenAI
Authors: Zeda Xu and Nikolas Martelaro and Christopher McComb
Published in: arXiv preprint arXiv:2511.03131, 2025

With generative AI-powered design tools, designers and engineers can efficiently generate large numbers of design ideas. However, efficient exploration of these ideas requires designers to select a smaller group of potential solutions for further development. Therefore, the ability to judge and evaluate designs is critical for the successful use of generative design tools. Different design representation modalities can potentially affect designers' judgments. This work investigates how different design modalities, including visual rendering, numerical performance data, and a combination of both, affect designers' design selections from AI-generated design concepts for Uncrewed Aerial Vehicles. We found that different design modalities do affect designers' choices. Unexpectedly, we found that providing only numerical design performance data can lead to the best ability to select optimal designs. We also found that participants prefer visually conventional designs with axis-symmetry. The findings of this work provide insights into the interaction between human users and generative design systems.
A Real-Time Automatic Interaction Dynamics Notation Communication Analysis System
Authors: Elizabeth S McGee and Eric Brubaker and Jonathan Cagan and Christopher McComb
Published in: International Design Engineering Technical Conferences and Computers and …, 2025

Effective teamwork is essential for engineering design, but communication challenges can hinder collaboration. Interaction Dynamics Notation (IDN) provides a structured framework for analyzing team interactions, but its manual coding process is time-consuming and impractical for real-time applications. This study presents an AI-driven system that automatically assigns IDN symbols in real-time using a combination of automatic speech recognition and a large language model (LLM). The system was tested during a NASA design sprint, achieving 80.3% alignment with human-coded IDN classifications—comparable to human inter-rater reliability. Results highlight the model’s strengths in identifying structured conversational patterns and its challenges with context-dependent interactions like humor and idea blocking. The findings demonstrate the feasibility of real-time AI-driven IDN classification, paving the way …
Community survey data of permafrost thaw, coastal erosion, and civil infrastructure damage in Point Lay, Wainwright, Utqiaġvik, and Kaktovik, Alaska. 29 October 2021–1 February 2022.
Authors: Min Liew and Ming Xiao and Louise Farquharson and Dmitry Nicolsky and Anne Jensen and Vladimir Romanovsky and Jana Peirce and Lilian Alessa and Christopher McComb and Xiong Zhang and Benjamin Jones
Published in: NSF Arctic Data Center, 2022

{"Abstract":["This dataset presents the results of a community survey that was designed to better understand the effects of permafrost degradation and coastal erosion on civil infrastructure. Observations were collected from residents in four Arctic coastal communities: Point Lay, Wainwright, Utqiaġvik, and Kaktovik. There are three categories of questions in this survey: permafrost degradation, coastal erosion, and infrastructure damage and repair. The participants identified changes in ground surface manifested by permafrost degradation in and around their communities. The options provided in the questionnaire included surface water ponding, sinkholes, ground surface collapse, differential ground settlement along roads and gravel pads, and others. The periods during which these changes have been happening were also recorded; the options include less than 6 months, 0.5–1 year, 1–3 years, 3–5 years, 5–10 years, and greater than 10 years. Participants also indicated the infrastructure types affected by permafrost degradation. The options include houses, runways, schools, ice cellars, water and sewer lines, and others. Effects of permafrost degradation on residential buildings, buried pipelines, utilidors, and roads were reported in the survey. Detailed information such as damage type, damage location, repair method, and effectiveness of repair methods was also recorded. For the questions related to coastal erosion, participants identified events of coastal erosion, periods during which coastal erosion has been happening, types of civil infrastructure affected, and types of erosion control measures implemented and their effectiveness …
Pressnet: A Forming Dataset for Structural Simulation in Pressed Blanks With Deep Learning Benchmarks
Authors: Prince Panta and Saroj Belbase and Bipin Shrestha and Nirmal Panta and Dikshya Parajuli and Rujal Acharya and Saugat Kafley and Amit Regmi and Ken Igeta and Akio Tanaka and Christopher McComb
Published in: International Design Engineering Technical Conferences and Computers and …, 2025

The press forming process is critical for manufacturing a wide array of products from materials such as metal and glass. Each forming process requires an accurate simulation beforehand to support design optimization and to prevent defects in the actual manufacturing of high-quality products. However, traditional finite element method (FEM) simulations that are commonly used to predict deformation and stress during the forming process are computationally intensive and time-consuming. Deep learning (DL) models have emerged as a promising alternative. These machine learning models can significantly reduce computational costs and provide fast inference once trained, which makes them attractive for large-scale and real-time applications.Despite the potential of DL in simulating press forming processes, there is no comprehensive, publicly available dataset that captures variability in deformation …
Energy-based feature extraction with adaptive local domain decomposition for prediction of transient and turbulence flow with operator regression models
Authors: Wenzhuo Xu and Madhav Karthikeyakannan and Christopher McComb and Noelia Grande Gutiérrez
Published in: Computers & Fluids, 106958, 2026

Machine learning (ML) based surrogate models offer the potential to accelerate real-world engineering simulations involving millions of elements by bypassing the need for full-scale numerical simulations. However, current model capacities and available GPU memory often impose severe constraints, limiting our ability to accurately represent the highly variant physical dynamics encountered in complex systems. In traditional numerical methods, these computational limitations are mitigated using domain decomposition. The computational domain is split up to enable parallelization of the computation and reduce memory load. Similarly, ML models can benefit from decomposing the domain into subdomains. However, domain decomposition alone is insufficient to guarantee model performance and accuracy when physical dynamics vary spatially. We introduce the Adaptive Local Domain Decomposition (ALDD …
Fast Super-Resolution Analysis of Low-Pressure Duct Air Flow 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: International Design Engineering Technical Conferences and Computers and …, 2025

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 …
KeepDelta: A Python Library for Human-Readable Data Differencing
Authors: Aslan Noorghasemi and Christopher McComb
Published in: Journal of Open Source Software, 2025

Efficiently managing evolving data is crucial in applications like computational simulations and sensing, where dynamic data tracking and processing are essential. In simulations, the traditional method known as full-state encoding stores the entire system state, including all nested data structures and variable values, at every timestep. While simple to implement, this approach is highly storage-intensive. On the other hand, recalculating states from scratch to avoid storage demands is computationally expensive. Similarly, in sensing, continuously transmitting full data snapshots is inefficient, leading to increased bandwidth consumption and latency. KeepDelta addresses this challenge by providing a lightweight Python library that captures and applies only the changes (deltas) between successive states of complex, nested Python data structures. Designed for clarity and ease of use, KeepDelta produces human-readable outputs, facilitating debugging and analysis in research applications.
The Impact of Design Representation on Equal Contribution in Engineering Design Teams
Authors: Rachel Shockley and Kai Herchenroether and Zeda Xu and Nikolas Martelaro and Christopher McComb
Published in: International Design Engineering Technical Conferences and Computers and …, 2025

Collaborative design in engineering is crucial, requiring input from multiple domains. Common design representations, such as CAD models, textual descriptions, and hand sketches, influence team behaviors and dynamics, potentially affecting participation equality. Prior research suggests that design artifact modalities impact collaboration by shaping communication patterns and decision-making, thereby influencing team success. Identifying the limitations of each representation allows for better selection in different contexts. This study examines three common design representations (CAD models, textual descriptions, and hand sketches) to assess their effect on equal participation in collaborative engineering design. Metrics such as speaking time, word count, and turn-taking were analyzed to detect correlations between contribution equality and design modality. Contrary to expectations, results showed no …