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@article{ma2023conceptualdesigngeneration,
abstract = {Concept generation is a creative step in the conceptual design phase, where designers often turn to brainstorming, mindmapping, or crowdsourcing design ideas to complement their own knowledge of the domain. Recent advances in natural language processing (NLP) and machine learning (ML) have led to the rise of Large Language Models (LLMs) capable of generating seemingly creative outputs from textual prompts. The success of these models has led to their integration and application across a variety of domains, including art, entertainment, and other creative work. In this paper, we leverage LLMs to generate solutions for a set of 12 design problems and compare them to a baseline of crowdsourced solutions. We evaluate the differences between generated and crowdsourced design solutions through multiple perspectives, including human expert evaluations and computational metrics. Expert …},
author = {Kevin Ma and Daniele Grandi and Christopher McComb and Kosa Goucher-Lambert},
citation = {International Design Engineering Technical Conferences and Computers and …, 2023},
conference = {International Design Engineering Technical Conferences and Computers and Information in Engineering Conference},
pub_year = {2023},
title = {Conceptual Design Generation Using Large Language Models}
}
|
{
"ENTRYTYPE": "article",
"ID": "ma2023conceptualdesigngeneration",
"abstract": "Concept generation is a creative step in the conceptual design phase, where designers often turn to brainstorming, mindmapping, or crowdsourcing design ideas to complement their own knowledge of the domain. Recent advances in natural language processing (NLP) and machine learning (ML) have led to the rise of Large Language Models (LLMs) capable of generating seemingly creative outputs from textual prompts. The success of these models has led to their integration and application across a variety of domains, including art, entertainment, and other creative work. In this paper, we leverage LLMs to generate solutions for a set of 12 design problems and compare them to a baseline of crowdsourced solutions. We evaluate the differences between generated and crowdsourced design solutions through multiple perspectives, including human expert evaluations and computational metrics. Expert …",
"author": "Kevin Ma and Daniele Grandi and Christopher McComb and Kosa Goucher-Lambert",
"citation": "International Design Engineering Technical Conferences and Computers and …, 2023",
"conference": "International Design Engineering Technical Conferences and Computers and Information in Engineering Conference",
"journal": null,
"number": null,
"pages": null,
"pub_year": 2023,
"publisher": null,
"title": "Conceptual Design Generation Using Large Language Models",
"volume": null
}
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/scholar?hl=en&cites=16608122350620813083
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https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings-abstract/IDETC-CIE2023/87349/1170650
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2025-06-14
| null | null | null | null | null | null | null | null | 2 | 33 | null |
@article{song2022whenfacedwith,
abstract = {As artificial intelligence (AI) assistance tools become more ubiquitous in engineering design, it becomes increasingly necessary to understand the influence of AI assistance on the design process and design effectiveness. Previous work has shown the advantages of incorporating AI design agents to assist human designers. However, the influence of AI assistance on the behavior of designers during the design process is still unknown. This study examines the differences in participants’ design process and effectiveness with and without AI assistance during a complex drone design task using the HyForm design research platform. Data collected from this study are analyzed to assess the design process and effectiveness using quantitative methods, such as hidden Markov models and network analysis. The results indicate that AI assistance is most beneficial when addressing moderately complex objectives but …},
author = {Binyang Song and Nicolas F Soria Zurita and Hannah Nolte and Harshika Singh and Jonathan Cagan and Christopher McComb},
citation = {Journal of Mechanical Design, 2022},
journal = {Journal of Mechanical Design},
pub_year = {2022},
title = {When faced with increasing complexity: The effectiveness of AI assistance for drone design}
}
|
{
"ENTRYTYPE": "article",
"ID": "song2022whenfacedwith",
"abstract": "As artificial intelligence (AI) assistance tools become more ubiquitous in engineering design, it becomes increasingly necessary to understand the influence of AI assistance on the design process and design effectiveness. Previous work has shown the advantages of incorporating AI design agents to assist human designers. However, the influence of AI assistance on the behavior of designers during the design process is still unknown. This study examines the differences in participants’ design process and effectiveness with and without AI assistance during a complex drone design task using the HyForm design research platform. Data collected from this study are analyzed to assess the design process and effectiveness using quantitative methods, such as hidden Markov models and network analysis. The results indicate that AI assistance is most beneficial when addressing moderately complex objectives but …",
"author": "Binyang Song and Nicolas F Soria Zurita and Hannah Nolte and Harshika Singh and Jonathan Cagan and Christopher McComb",
"citation": "Journal of Mechanical Design, 2022",
"conference": null,
"journal": "Journal of Mechanical Design",
"number": null,
"pages": null,
"pub_year": 2022,
"publisher": null,
"title": "When faced with increasing complexity: The effectiveness of AI assistance for drone design",
"volume": null
}
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https://asmedigitalcollection.asme.org/mechanicaldesign/article-abstract/144/2/021701/1114945
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2025-06-14
| null | null | null | null | null | null | null | 8 | 10 | 15 | null |
@article{goucher-lambert2019usinghiddenmarkov,
abstract = {Recently, design researchers have begun to use neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) to understand a variety of cognitive processes relevant to design. However, common neuroimaging analysis techniques require significant assumptions relating temporal and spatial information during model formulation. In this work, we apply hidden Markov Models (HMM) in order to uncover patterns of brain activation in a design-relevant fMRI dataset. The underlying fMRI data comes from a prior research study in which participants generated solutions for twelve open-ended design problems from the literature. HMMs are generative models that are able to automatically infer the internal state characteristics of a process by observing state emissions. In this work, we demonstrate that distinct states can be extracted from the design ideation fMRI dataset, and that designers are likely to …},
author = {Kosa Goucher-Lambert and Christopher McComb},
citation = {International Conference on Engineering Design, 2019},
conference = {International Conference on Engineering Design},
pub_year = {2019},
title = {Using Hidden Markov Models to Uncover Underlying States in Neuroimaging Data for a Design Ideation Task}
}
|
{
"ENTRYTYPE": "article",
"ID": "goucher-lambert2019usinghiddenmarkov",
"abstract": "Recently, design researchers have begun to use neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) to understand a variety of cognitive processes relevant to design. However, common neuroimaging analysis techniques require significant assumptions relating temporal and spatial information during model formulation. In this work, we apply hidden Markov Models (HMM) in order to uncover patterns of brain activation in a design-relevant fMRI dataset. The underlying fMRI data comes from a prior research study in which participants generated solutions for twelve open-ended design problems from the literature. HMMs are generative models that are able to automatically infer the internal state characteristics of a process by observing state emissions. In this work, we demonstrate that distinct states can be extracted from the design ideation fMRI dataset, and that designers are likely to …",
"author": "Kosa Goucher-Lambert and Christopher McComb",
"citation": "International Conference on Engineering Design, 2019",
"conference": "International Conference on Engineering Design",
"journal": null,
"number": null,
"pages": null,
"pub_year": 2019,
"publisher": null,
"title": "Using Hidden Markov Models to Uncover Underlying States in Neuroimaging Data for a Design Ideation Task",
"volume": null
}
|
0P9w_S0AAAAJ:SeFeTyx0c_EC
| 32 |
/scholar?hl=en&cites=5818249137206786151
|
[
"5818249137206786151"
] |
https://www.cambridge.org/core/journals/proceedings-of-the-international-conference-on-engineering-design/article/using-hidden-markov-models-to-uncover-underlying-states-in-neuroimaging-data-for-a-design-ideation-task/FFECA47BD9133021144EA4BF37D40BFA
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2025-06-14
| null | null | null | null | 1 | 8 | 6 | 6 | 6 | 5 | null |
@article{mccomb2022aconceptualframework,
abstract = {The design community traditionally excels at multidisciplinary research efforts, integrating work from social science, engineering, computer science, and other disciplines. Simultaneously, design is also developing as a discipline in its own right. In order to balance a multidisciplinary 1 tradition against consolidation around a core set of disciplinary foundations, the design community must make intentional decisions about engaging in multidisciplinary research. Towards this end, we propose the Degrees of Disciplinarity Framework, a lingua franca for describing varying relationships between disciplines in a given research effort. We also introduce research archetypes in relation to this conceptual …},
author = {Christopher McComb and Kathryn Jablokow},
citation = {Design Studies, 2022},
journal = {Design Studies},
pub_year = {2022},
title = {A Conceptual Framework for Multidisciplinary Design Research with Example Application to Agent-Based Modeling}
}
|
{
"ENTRYTYPE": "article",
"ID": "mccomb2022aconceptualframework",
"abstract": "The design community traditionally excels at multidisciplinary research efforts, integrating work from social science, engineering, computer science, and other disciplines. Simultaneously, design is also developing as a discipline in its own right. In order to balance a multidisciplinary 1 tradition against consolidation around a core set of disciplinary foundations, the design community must make intentional decisions about engaging in multidisciplinary research. Towards this end, we propose the Degrees of Disciplinarity Framework, a lingua franca for describing varying relationships between disciplines in a given research effort. We also introduce research archetypes in relation to this conceptual …",
"author": "Christopher McComb and Kathryn Jablokow",
"citation": "Design Studies, 2022",
"conference": null,
"journal": "Design Studies",
"number": null,
"pages": null,
"pub_year": 2022,
"publisher": null,
"title": "A Conceptual Framework for Multidisciplinary Design Research with Example Application to Agent-Based Modeling",
"volume": null
}
|
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https://www.sciencedirect.com/science/article/pii/S0142694X21000855
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2025-06-14
| null | null | null | null | null | null | null | 12 | 12 | 7 | null |
@article{krishnakumar2022usingworkplacethriving,
abstract = {During the onset of the COVID‐19 crisis, universities rapidly pivoted to online formats and were often unable to adhere to the best practices of online learning highlighted in prior literature. It is well documented that a variety of barriers impeded “normal” educational practices.The purpose of this paper is to investigate the perceptions of first‐year engineering students enrolled in an introductory engineering design course during the rapid transition to online working environments. We view students' perceptions through the theoretical lens of workplace thriving theory, a framework that allowed us to capture aspects of education required for students to thrive in non‐optimum learning settings.This research employed semi‐structured interview methods with 13 students enrolled in an introductory engineering design course that relies on project‐based team learning. We …},
author = {Sandeep Krishnakumar and Torsten Maier and Catherine GP Berdanier and Sarah Ritter and Christopher McComb and Jessica Menold},
citation = {Journal of Engineering Education, 2022},
journal = {Journal of Engineering Education},
pub_year = {2022},
title = {Using Workplace Thriving Theory to Investigate First-Year Engineering Students’ Abilities to Thrive during the Transition to Online Learning due to COVID-19}
}
|
{
"ENTRYTYPE": "article",
"ID": "krishnakumar2022usingworkplacethriving",
"abstract": "During the onset of the COVID‐19 crisis, universities rapidly pivoted to online formats and were often unable to adhere to the best practices of online learning highlighted in prior literature. It is well documented that a variety of barriers impeded “normal” educational practices.The purpose of this paper is to investigate the perceptions of first‐year engineering students enrolled in an introductory engineering design course during the rapid transition to online working environments. We view students' perceptions through the theoretical lens of workplace thriving theory, a framework that allowed us to capture aspects of education required for students to thrive in non‐optimum learning settings.This research employed semi‐structured interview methods with 13 students enrolled in an introductory engineering design course that relies on project‐based team learning. We …",
"author": "Sandeep Krishnakumar and Torsten Maier and Catherine GP Berdanier and Sarah Ritter and Christopher McComb and Jessica Menold",
"citation": "Journal of Engineering Education, 2022",
"conference": null,
"journal": "Journal of Engineering Education",
"number": null,
"pages": null,
"pub_year": 2022,
"publisher": null,
"title": "Using Workplace Thriving Theory to Investigate First-Year Engineering Students’ Abilities to Thrive during the Transition to Online Learning due to COVID-19",
"volume": null
}
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https://onlinelibrary.wiley.com/doi/abs/10.1002/jee.20447
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2025-06-14
| null | null | null | null | null | null | null | 6 | 14 | 7 | null |
@article{li2022usingphysics-informedgenerative,
abstract = {Computational fluid dynamics (CFD) simulations are useful in the field of engineering design as they provide deep insights on product or system performance without the need to construct and test physical prototypes. However, they can be very computationally intensive to run. Machine learning methods have been shown to reconstruct high-resolution single-phase turbulent fluid flow simulations from low-resolution inputs. This offers a potential avenue towards alleviating computational cost in iterative engineering design applications. However, little work thus far has explored the application of machine learning image super-resolution methods to multiphase fluid flow (which is important for emerging fields such as marine hydrokinetic energy conversion). In this work, we apply a modified version of the super-resolution generative adversarial network (SRGAN) model to a multiphase turbulent fluid flow problem …},
author = {Matthew Li and Christopher McComb},
citation = {Journal of Computing and Information Science in Engineering, 2022},
journal = {Journal of Computing and Information Science in Engineering},
pub_year = {2022},
title = {Using Physics-Informed Generative Adversarial Networks to Perform Super-Resolution for Multiphase Fluid Simulations}
}
|
{
"ENTRYTYPE": "article",
"ID": "li2022usingphysics-informedgenerative",
"abstract": "Computational fluid dynamics (CFD) simulations are useful in the field of engineering design as they provide deep insights on product or system performance without the need to construct and test physical prototypes. However, they can be very computationally intensive to run. Machine learning methods have been shown to reconstruct high-resolution single-phase turbulent fluid flow simulations from low-resolution inputs. This offers a potential avenue towards alleviating computational cost in iterative engineering design applications. However, little work thus far has explored the application of machine learning image super-resolution methods to multiphase fluid flow (which is important for emerging fields such as marine hydrokinetic energy conversion). In this work, we apply a modified version of the super-resolution generative adversarial network (SRGAN) model to a multiphase turbulent fluid flow problem …",
"author": "Matthew Li and Christopher McComb",
"citation": "Journal of Computing and Information Science in Engineering, 2022",
"conference": null,
"journal": "Journal of Computing and Information Science in Engineering",
"number": null,
"pages": null,
"pub_year": 2022,
"publisher": null,
"title": "Using Physics-Informed Generative Adversarial Networks to Perform Super-Resolution for Multiphase Fluid Simulations",
"volume": null
}
|
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"6987329233899323240"
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https://asmedigitalcollection.asme.org/computingengineering/article-abstract/22/4/044501/1133478
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2025-06-14
| null | null | null | null | null | null | null | 4 | 9 | 15 | null |
@article{mehta2020miningdesignheuristics,
abstract = {In this research, we collected eye-tracking data from nine engineering graduate students as they redesigned a traditionally manufactured part for additive manufacturing (AM). Final artifacts were assessed for manufacturability and quality of final design, and design behaviors were captured via the eye-tracking data. Statistical analysis of design behavior duration shows that participants with more than 3 years of industry experience spend significantly less time removing material and revising than those with less experience. Hidden Markov modeling (HMM) analysis of the design behaviors gives insight to the transitions between behaviors through which designers proceed. Findings show that high-performing designers proceeded through four behavioral states, smoothly transitioning between states. In contrast, low-performing designers roughly transitioned between states, with moderate transition probabilities …},
author = {Priyesh Mehta and Manoj Malviya and Christopher McComb and Guha Manogharan and Catherine GP Berdanier},
citation = {Journal of Mechanical Design 142 (12), 2020},
journal = {Journal of Mechanical Design},
number = {12},
pub_year = {2020},
publisher = {American Society of Mechanical Engineers Digital Collection},
title = {Mining Design Heuristics for Additive Manufacturing Via Eye-Tracking Methods and Hidden Markov Modeling},
volume = {142}
}
|
{
"ENTRYTYPE": "article",
"ID": "mehta2020miningdesignheuristics",
"abstract": "In this research, we collected eye-tracking data from nine engineering graduate students as they redesigned a traditionally manufactured part for additive manufacturing (AM). Final artifacts were assessed for manufacturability and quality of final design, and design behaviors were captured via the eye-tracking data. Statistical analysis of design behavior duration shows that participants with more than 3 years of industry experience spend significantly less time removing material and revising than those with less experience. Hidden Markov modeling (HMM) analysis of the design behaviors gives insight to the transitions between behaviors through which designers proceed. Findings show that high-performing designers proceeded through four behavioral states, smoothly transitioning between states. In contrast, low-performing designers roughly transitioned between states, with moderate transition probabilities …",
"author": "Priyesh Mehta and Manoj Malviya and Christopher McComb and Guha Manogharan and Catherine GP Berdanier",
"citation": "Journal of Mechanical Design 142 (12), 2020",
"conference": null,
"journal": "Journal of Mechanical Design",
"number": "12",
"pages": null,
"pub_year": 2020,
"publisher": "American Society of Mechanical Engineers Digital Collection",
"title": "Mining Design Heuristics for Additive Manufacturing Via Eye-Tracking Methods and Hidden Markov Modeling",
"volume": "142"
}
|
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https://asmedigitalcollection.asme.org/mechanicaldesign/article-abstract/142/12/124502/1086965
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2025-06-14
| null | null | null | null | null | 1 | 6 | 4 | 11 | 5 | null |
@article{bayrak2021astrategicdecision-making,
abstract = {Advances in artificial intelligence create new opportunities for computers to support humans as peers in hybrid teams in several complex problem-solving situations. This paper proposes a decision-making architecture for adaptively informing decisions in human-computer collaboration for large-scale competitive problems under dynamic environments. The proposed architecture integrates methods from sequence learning, model predictive control, and game theory. Computers in this architecture learn objectives and strategies from experimental data to support humans with strategic decisions while operational decisions are made by humans. The paper also presents data-driven methods for partitioning tasks among a team of computers in this architecture. The generalized methodology is illustrated on the real-time strategy game Starcraft II. The results from this application show that low-performing players can …},
author = {Alparslan Emrah Bayrak and Christopher McComb and Kenneth Kotovsky and Jonathan Cagan},
citation = {Decision Support Systems, 2021},
journal = {Decision Support Systems},
pub_year = {2021},
title = {A Strategic Decision-making Architecture Toward Hybrid Teams for Dynamic Competitive Problems}
}
|
{
"ENTRYTYPE": "article",
"ID": "bayrak2021astrategicdecision-making",
"abstract": "Advances in artificial intelligence create new opportunities for computers to support humans as peers in hybrid teams in several complex problem-solving situations. This paper proposes a decision-making architecture for adaptively informing decisions in human-computer collaboration for large-scale competitive problems under dynamic environments. The proposed architecture integrates methods from sequence learning, model predictive control, and game theory. Computers in this architecture learn objectives and strategies from experimental data to support humans with strategic decisions while operational decisions are made by humans. The paper also presents data-driven methods for partitioning tasks among a team of computers in this architecture. The generalized methodology is illustrated on the real-time strategy game Starcraft II. The results from this application show that low-performing players can …",
"author": "Alparslan Emrah Bayrak and Christopher McComb and Kenneth Kotovsky and Jonathan Cagan",
"citation": "Decision Support Systems, 2021",
"conference": null,
"journal": "Decision Support Systems",
"number": null,
"pages": null,
"pub_year": 2021,
"publisher": null,
"title": "A Strategic Decision-making Architecture Toward Hybrid Teams for Dynamic Competitive Problems",
"volume": null
}
|
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"16473698746400821994"
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https://www.sciencedirect.com/science/article/pii/S0167923620302451
|
/scholar?oi=bibs&hl=en&q=related:6tIEfLpVnuQJ:scholar.google.com/
|
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2025-06-14
| null | null | null | null | null | null | 2 | 8 | 6 | 9 | null |
@article{alzayed2020candesignteams,
abstract = {Research on empathy has been surging in popularity in the engineering design community since empathy is known to help designers develop a deeper understanding of the users’ needs. Because of this, the design community has been invested in devising and assessing empathic design activities. However, research on empathy has been primarily limited to individuals, meaning we do not know how it impacts team performance, particularly in the concept generation and selection stages of the design process. Specifically, it is unknown how the empathic composition of teams, average (elevation) and standard deviation (diversity) of team members’ empathy, would impact design outcomes in the concept generation and selection stages of the design process. Therefore, the goal of the current study was to investigate the impact of team trait empathy on concept generation and selection in an engineering …},
author = {Mohammad Alsager Alzayed and Scarlett R Miller and Jessica Menold and Jacquelyn Huff and Christopher McComb},
citation = {ASME 2020 International Design Engineering Technical Conferences and …, 2020},
conference = {ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference},
pub_year = {2020},
title = {Can Design Teams Be Empathically Creative? A Simulation-Based Investigation on the Role of Team Empathy on Concept Generation and Selection}
}
|
{
"ENTRYTYPE": "article",
"ID": "alzayed2020candesignteams",
"abstract": "Research on empathy has been surging in popularity in the engineering design community since empathy is known to help designers develop a deeper understanding of the users’ needs. Because of this, the design community has been invested in devising and assessing empathic design activities. However, research on empathy has been primarily limited to individuals, meaning we do not know how it impacts team performance, particularly in the concept generation and selection stages of the design process. Specifically, it is unknown how the empathic composition of teams, average (elevation) and standard deviation (diversity) of team members’ empathy, would impact design outcomes in the concept generation and selection stages of the design process. Therefore, the goal of the current study was to investigate the impact of team trait empathy on concept generation and selection in an engineering …",
"author": "Mohammad Alsager Alzayed and Scarlett R Miller and Jessica Menold and Jacquelyn Huff and Christopher McComb",
"citation": "ASME 2020 International Design Engineering Technical Conferences and …, 2020",
"conference": "ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference",
"journal": null,
"number": null,
"pages": null,
"pub_year": 2020,
"publisher": null,
"title": "Can Design Teams Be Empathically Creative? A Simulation-Based Investigation on the Role of Team Empathy on Concept Generation and Selection",
"volume": null
}
|
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https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings-abstract/IDETC-CIE2020/1089999
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2025-06-14
| null | null | null | null | null | 1 | 6 | 6 | 9 | 4 | null |
@article{mccomb2018towardstherapid,
abstract = {The design of a system commits a significant portion of the final cost of that system. Many computational approaches have been developed to assist designers in the analysis (e.g., computational fluid dynamics) and synthesis (e.g., topology optimization) of engineered systems.},
author = {Christopher McComb},
citation = {Eight International Conference on Design Computing and Cognition, 2018},
conference = {Eight International Conference on Design Computing and Cognition},
pub_year = {2018},
title = {Towards the Rapid Design of Engineered Systems Through Deep Neural Networks}
}
|
{
"ENTRYTYPE": "article",
"ID": "mccomb2018towardstherapid",
"abstract": "The design of a system commits a significant portion of the final cost of that system. Many computational approaches have been developed to assist designers in the analysis (e.g., computational fluid dynamics) and synthesis (e.g., topology optimization) of engineered systems.",
"author": "Christopher McComb",
"citation": "Eight International Conference on Design Computing and Cognition, 2018",
"conference": "Eight International Conference on Design Computing and Cognition",
"journal": null,
"number": null,
"pages": null,
"pub_year": 2018,
"publisher": null,
"title": "Towards the Rapid Design of Engineered Systems Through Deep Neural Networks",
"volume": null
}
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2025-06-14
| null | null | null | 4 | 4 | 6 | 4 | 5 | 1 | 2 | null |
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