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Journal Articles

Importance-Induced Customer Segmentation Using Explainable Machine Learning

Seyoung Park, Yilan Jiang, Harrison Kim

Publisher: ASME

J. Mech. Des.

Paper No: MD-24-1118

https://doi.org/10.1115/1.4066746

Published Online: September 30, 2024

... in the J ournal of M echanical D esign . 24 02 2024 15 09 2024 23 09 2024 30 09 2024 Email: seyoung7@illinois.edu Email: yilanj2@illinois.edu Email: hmkim@illinois.edu Data-driven design design automation Accepted Manuscript Not Copyedited Journal...

View Article titled, Importance-Induced Customer Segmentation Using Explainable Machine Learning

Topics: Image segmentation, Machine learning, Construction, Data-driven design, Design automation, Neural network models, Product development, Trend analysis

Journal Articles

Designing Connectivity-Guaranteed Porous Metamaterial Units Using Generative Graph Neural Networks

Zihan Wang, Austin Bray, Kiarash Naghavi Khanghah, Hongyi Xu

Publisher: ASME

J. Mech. Des. February 2025, 147(2): 021706.

Paper No: MD-24-1409

https://doi.org/10.1115/1.4066128

Published Online: September 26, 2024

... Graphical Abstract Figure porous metamaterial graph neural network variational graph autoencoder design optimization generative design connectivity data-driven design design of engineered materials system machine learning Division of Civil, Mechanical and Manufacturing Innovation...

View Article titled, Designing Connectivity-Guaranteed Porous Metamaterial Units Using Generative Graph Neural Networks

Topics: Design, Metamaterials, Voxel, Thermal conductivity, Graph neural networks

Journal Articles

An automated data-driven approach for product design strategies to respond to market disruption following COVID-19

Seyoung Park, Kangcheng Lin, Junegak Joung, Harrison Kim

Publisher: ASME

J. Mech. Des.

Paper No: MD-24-1210

https://doi.org/10.1115/1.4066684

Published Online: September 25, 2024

... 2024 12 09 2024 25 09 2024 Email: seyoung7@illinois.edu Email: klin14@illinois.edu Email: june30@hanyang.ac.kr Email: hmkim@illinois.edu Data-driven design design automation Accepted Manuscript Not Copyedited Journal of Mechanical Design. Received April 02, 2024...

View Article titled, An automated data-driven approach for product design strategies to respond to market disruption following COVID-19

Topics: Product design, Laptop computers, Pandemics, Preferences, Batteries, Data collection, Data-driven design, Design automation, Machine learning

Journal Articles

NURBS-OT: An Advanced Model for Generative Curve Modeling

Shaoliang Yang, Jun Wang, Kang Wang

Publisher: ASME

J. Mech. Des.

Paper No: MD-24-1312

https://doi.org/10.1115/1.4066549

Published Online: September 13, 2024

... of M echanical D esign . 10 05 2024 03 09 2024 05 09 2024 13 09 2024 Data-driven design design automation generative design Machine learning Division of Civil, Mechanical and Manufacturing Innovation 10.13039/100000147 2245299 Journal of Mechanical...

View Article titled, NURBS-OT: An Advanced Model for Generative Curve Modeling

Topics: Modeling, B-splines, Generative adversarial networks, Airfoils, Computer-aided design, Computers, Data-driven design, Deep learning, Design automation, Generative design

Journal Articles

Multi-Objective Counterfactuals for Design: A Model-Agnostic Counterfactual Search Method for Multi-Modal Design Modifications

Lyle Regenwetter, Yazan Abu Obaideh, Faez Ahmed

Publisher: ASME

J. Mech. Des. February 2025, 147(2): 021401.

Paper No: MD-23-1610

https://doi.org/10.1115/1.4065998

Published Online: August 28, 2024

.... Not surprisingly, designers often struggle with the available choices. artificial intelligence data-driven design design automation design optimization generative design machine learning multi-objective optimization Graphical Abstract Figure 31 08 2023 24 06 2024 11 07...

View Article titled, Multi-Objective Counterfactuals for Design: A Model-Agnostic Counterfactual Search Method for Multi-Modal Design Modifications

Topics: Design, Optimization

Journal Articles

Mean Squared Error May Lead You Astray When Optimizing Your Inverse Design Methods

Milad Habibi, Shai Bernard, Jun Wang, Mark Fuge

Publisher: ASME

J. Mech. Des. February 2025, 147(2): 021701.

Paper No: MD-23-1702

https://doi.org/10.1115/1.4066102

Published Online: August 28, 2024

... Graphical Abstract Figure data-driven design design optimization machine learning topology optimization Advanced Research Projects Agency - Energy 10.13039/100006133 DE-AR0001216 Directorate for Engineering 10.13039/100000084 1943699 Design optimization...

View Article titled, Mean Squared Error May Lead You Astray When Optimizing Your Inverse Design Methods

Topics: Design, Heat conduction, Optimization, Topology optimization, Cantilever beams, Errors, Trajectories (Physics)

Includes: Supplementary data

Journal Articles

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Anirban Samaddar, Sandipp Krishnan Ravi, Nesar Ramachandra, Lele Luan, Sandeep Madireddy, Anindya Bhaduri, Piyush Pandita, Changjie Sun, Liping Wang

Publisher: ASME

J. Mech. Des.

Paper No: MD-24-1156

https://doi.org/10.1115/1.4066224

Published Online: August 16, 2024

... Email: wangli@ge.com Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 13 03 2024 27 07 2024 01 08 2024 16 08 2024 artificial intelligence Data-driven design Machine learning robust design...

View Article titled, Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Topics: Metamodeling, Modeling, Stress, Simulation, Tensors, Artificial intelligence, Artificial neural networks, Compression, Convolutional neural networks, Data-driven design

Journal Articles

Multi-Fidelity Design Framework Integrating Compositional Kernels to Facilitate Early-Stage Design Exploration of Complex Systems

Nikoleta Dimitra Charisi, Hans Hopman, Austin A. Kana

Publisher: ASME

J. Mech. Des. January 2025, 147(1): 011701.

Paper No: MD-24-1071

https://doi.org/10.1115/1.4065890

Published Online: July 23, 2024

... 2024 13 06 2024 01 07 2024 23 07 2024 1 Corresponding author. Email: N.D.Charisi@tudelft.nl Email: J.J.Hopman@tudelft.nl Email: A.A.Kana@tudelft.nl conceptual design data-driven design design space exploration multi-fidelity Gaussian processes compositional...

View Article titled, Multi-Fidelity Design Framework Integrating Compositional Kernels to Facilitate Early-Stage Design Exploration of Complex Systems

Topics: Design, Hafnium

Journal Articles

An Integrated Approach to Designing Robust Gas-Bearing Supported Turbocompressors Through Surrogate Modeling and Constrained All-At-Once Multi-Objective Optimization

Soheyl Massoudi, Cyril Picard, Jürg Schiffmann

Publisher: ASME

J. Mech. Des. December 2024, 146(12): 121706.

Paper No: MD-24-1183

https://doi.org/10.1115/1.4065823

Published Online: July 19, 2024

... constrained multi-objective optimization robustness metrics computational efficiency artificial intelligence data-driven design design automation design for manufacturing design integration design of multiscale systems design optimization machine learning metamodeling Designing...

View Article titled, An Integrated Approach to Designing Robust Gas-Bearing Supported Turbocompressors Through Surrogate Modeling and Constrained All-At-Once Multi-Objective Optimization

Topics: Artificial neural networks, Bearings, Blades, Compressors, Design, Gas bearings, Manufacturing, Optimization, Robustness, Rotors

Journal Articles

FrameGraph: A Scalable Performance Evaluation Method for Frame Structure Designs Using Graph Neural Network

Wenbin Hou, Yongcheng Li, Changsheng Wang

Publisher: ASME

J. Mech. Des. December 2024, 146(12): 121703.

Paper No: MD-23-1899

https://doi.org/10.1115/1.4065612

Published Online: June 18, 2024

... structural topology graph neural network GNN design space computer-aided engineering conceptual design data-driven design design evaluation design validation References [1] Silver , D. , Huang , A. , Maddison , C. J. , Guez , A. , Sifre , L. , Van Den...

View Article titled, FrameGraph: A Scalable Performance Evaluation Method for Frame Structure Designs Using Graph Neural Network

Topics: Algorithms, Artificial neural networks, Design, Dimensions, Performance evaluation, Topology, Finite element methods, Door frames

Journal Articles

Asynchronous Multi-Information Source Bayesian Optimization

Danial Khatamsaz, Raymundo Arroyave, Douglas L. Allaire

Publisher: ASME

J. Mech. Des. October 2024, 146(10): 101708.

Paper No: MD-23-1722

https://doi.org/10.1115/1.4065064

Published Online: April 9, 2024

... 09 04 2024 Graphical Abstract Figure data-driven design design of experiments design optimization design process machine learning simulation-based design Advanced Research Projects Agency - Energy 10.13039/100006133 DE-AR0001427 National Science Foundation...

View Article titled, Asynchronous Multi-Information Source Bayesian Optimization

Topics: Design, Optimization, Simulation, Performance

Journal Articles

Latent Crossover for Data-Driven Multifidelity Topology Design

Taisei Kii, Kentaro Yaji, Kikuo Fujita, Zhenghui Sha, Carolyn Conner Seepersad

Publisher: ASME

J. Mech. Des. May 2024, 146(5): 051713.

Paper No: MD-23-1482

https://doi.org/10.1115/1.4064979

Published Online: March 28, 2024

... that exhibit high levels of performance. Japan Society for the Promotion of Science 10.13039/501100001691 20H02054 20KK0329 23H03799 topology optimization deep generative model latent crossover maximum stress minimization data-driven design machine learning multiobjective...

View Article titled, Latent Crossover for Data-Driven Multifidelity Topology Design

Topics: Deep generative models, Design, Optimization, Stress, Topology, Topology optimization, Evolutionary algorithms

Journal Articles

Data-Driven Car Drag Prediction With Depth and Normal Renderings

Binyang Song, Chenyang Yuan, Frank Permenter, Nikos Arechiga, Faez Ahmed

Publisher: ASME

J. Mech. Des. May 2024, 146(5): 051714.

Paper No: MD-23-1544

https://doi.org/10.1115/1.4065063

Published Online: March 28, 2024

... simple 2D perspective renderings for drag coefficient prediction. To train our model, we also assemble a diverse dataset of high-quality 3D car meshes and simulate their drag coefficients using CFD simulations. This dataset, upon public release, can drive the development of other data-driven design...

View Article titled, Data-Driven Car Drag Prediction With Depth and Normal Renderings

Topics: Computational fluid dynamics, Drag (Fluid dynamics), Shapes, Simulation, Design

Journal Articles

Multi-Fidelity Design of Porous Microstructures for Thermofluidic Applications

Jonathan Tammer Eweis-Labolle, Chuanning Zhao, Yoonjin Won, Ramin Bostanabad

Publisher: ASME

J. Mech. Des. October 2024, 146(10): 101705.

Paper No: MD-23-1728

https://doi.org/10.1115/1.4064813

Published Online: March 18, 2024

... of M echanical D esign . 17 10 2023 12 02 2024 13 02 2024 18 03 2024 Graphical Abstract Figure Gaussian processes multi-fidelity modeling microstructure reconstruction inverse problems thermal management data-driven design design automation design...

View Article titled, Multi-Fidelity Design of Porous Microstructures for Thermofluidic Applications

Topics: Design, Modeling

Journal Articles

MeshPointNet: 3D Surface Classification Using Graph Neural Networks and Conformal Predictions on Mesh-Based Representations

Amin Heyrani Nobari, Justin Rey, Suhas Kodali, Matthew Jones, Faez Ahmed

Publisher: ASME

J. Mech. Des. May 2024, 146(5): 051712.

Paper No: MD-23-1477

https://doi.org/10.1115/1.4064673

Published Online: March 18, 2024

... networks conformal predictions GNN mesh segmentation 3D model segmentation CFD data-driven design design automation design evaluation simulation-based design In the field of engineering design, it’s common for engineers to depict their designs using 3D computer-aided design (CAD...

View Article titled, MeshPointNet: 3D Surface Classification Using Graph Neural Networks and Conformal Predictions on Mesh-Based Representations

Topics: Aircraft, Computational fluid dynamics, Computer-aided design, Deep learning, Image segmentation, Simulation, Artificial neural networks, Design

Journal Articles

Multi-Task Learning for Design Under Uncertainty With Multi-Fidelity Partially Observed Information

Yanwen Xu, Hao Wu, Zheng Liu, Pingfeng Wang, Yumeng Li

Publisher: ASME

J. Mech. Des. August 2024, 146(8): 081704.

Paper No: MD-23-1618

https://doi.org/10.1115/1.4064492

Published Online: March 5, 2024

... 03 2024 Graphical Abstract Figure multi-task learning partially observed information reliability-based design optimization uncertainty quantification data-driven design design automation design optimization machine learning metamodeling reliability in design systems...

View Article titled, Multi-Task Learning for Design Under Uncertainty With Multi-Fidelity Partially Observed Information

Topics: Design, Design under uncertainty, Machine learning, Metamodeling, Optimization, Reliability, Reliability-based optimization, Uncertainty, Complex systems, Uncertainty quantification

Journal Articles

Data-Driven Global Sensitivity Analysis of Variable Groups for Understanding Complex Physical Interactions in Engineering Design

Tuba Dolar, Doksoo Lee, Wei Chen

Publisher: ASME

J. Mech. Des. September 2024, 146(9): 091703.

Paper No: MD-23-1749

https://doi.org/10.1115/1.4064633

Published Online: March 5, 2024

... 2024 Graphical Abstract Figure data-driven design global sensitivity analysis interpretable machine learning artificial neural networks grouped global sensitivity indices sensitivity analysis for design Global sensitivity analysis (GSA) is widely used in engineering...

View Article titled, Data-Driven Global Sensitivity Analysis of Variable Groups for Understanding Complex Physical Interactions in Engineering Design

Topics: Artificial neural networks, Design, Sensitivity analysis, Engineering design, Machine learning

Journal Articles

TCN-GAWO: Genetic Algorithm Enhanced Weight Optimization for Temporal Convolutional Network

Shuhuai Gu, Qi Xi, Jing Wang, Peizhen Qiu, Mian Li

Publisher: ASME

J. Mech. Des. October 2024, 146(10): 101703.

Paper No: MD-23-1269

https://doi.org/10.1115/1.4064809

Published Online: March 5, 2024

... 05 2023 08 02 2024 13 02 2024 05 03 2024 Graphical Abstract Figure artificial intelligence data-driven design machine learning To solve the problem that the traditional convolutional neural network (CNN) cannot deal with time series data well, researchers...

View Article titled, TCN-GAWO: Genetic Algorithm Enhanced Weight Optimization for Temporal Convolutional Network

Topics: Genetic algorithms, Optimization, Time series, Weight (Mass), Artificial neural networks, Lubrication

Journal Articles

Untrained and Unmatched: Fast and Accurate Zero-Training Classification for Tabular Engineering Data

Cyril Picard, Faez Ahmed

Publisher: ASME

J. Mech. Des. September 2024, 146(9): 091705.

Paper No: MD-23-1678

https://doi.org/10.1115/1.4064811

Published Online: March 5, 2024

.../ Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 22 09 2023 12 02 2024 13 02 2024 05 03 2024 Graphical Abstract Figure artificial intelligence data-driven design design evaluation machine learning...

View Article titled, Untrained and Unmatched: Fast and Accurate Zero-Training Classification for Tabular Engineering Data

Topics: Design, Engineering design, Machine learning, Trains, Algorithms, Tradeoffs, Solar energy, Optimization, Uncertainty

Journal Articles

Using AI-Enabled Divergence and Convergence Patterns as a Quantitative Artifact in Design Education

Matt Chiu, Wang Lin Sim, Nigel Mun, Arlindo Silva

Publisher: ASME

J. Mech. Des. March 2024, 146(3): 032301.

Paper No: MD-23-1261

https://doi.org/10.1115/1.4064262

Published Online: January 29, 2024

...: arlindo_silva@sutd.edu.sg Contributed by the Design Education Committee of ASME for publication in the J ournal of M echanical D esign . 03 05 2023 21 11 2023 27 11 2023 29 01 2024 artificial intelligence cognitive-based design data-driven design decision theory design...

View Article titled, Using AI-Enabled Divergence and Convergence Patterns as a Quantitative Artifact in Design Education

Topics: Design, Design education, Students, Visualization, Engineering teachers, Education, Natural language processing

Journal citation

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Supplemental Material

Manuscript

Importance-Induced Customer Segmentation Using Explainable Machine Learning

Designing Connectivity-Guaranteed Porous Metamaterial Units Using Generative Graph Neural Networks

An automated data-driven approach for product design strategies to respond to market disruption following COVID-19

NURBS-OT: An Advanced Model for Generative Curve Modeling

Multi-Objective Counterfactuals for Design: A Model-Agnostic Counterfactual Search Method for Multi-Modal Design Modifications

Mean Squared Error May Lead You Astray When Optimizing Your Inverse Design Methods

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Multi-Fidelity Design Framework Integrating Compositional Kernels to Facilitate Early-Stage Design Exploration of Complex Systems

An Integrated Approach to Designing Robust Gas-Bearing Supported Turbocompressors Through Surrogate Modeling and Constrained All-At-Once Multi-Objective Optimization

FrameGraph: A Scalable Performance Evaluation Method for Frame Structure Designs Using Graph Neural Network

Asynchronous Multi-Information Source Bayesian Optimization

Latent Crossover for Data-Driven Multifidelity Topology Design

Data-Driven Car Drag Prediction With Depth and Normal Renderings

Multi-Fidelity Design of Porous Microstructures for Thermofluidic Applications

MeshPointNet: 3D Surface Classification Using Graph Neural Networks and Conformal Predictions on Mesh-Based Representations

Multi-Task Learning for Design Under Uncertainty With Multi-Fidelity Partially Observed Information

Data-Driven Global Sensitivity Analysis of Variable Groups for Understanding Complex Physical Interactions in Engineering Design

TCN-GAWO: Genetic Algorithm Enhanced Weight Optimization for Temporal Convolutional Network

Untrained and Unmatched: Fast and Accurate Zero-Training Classification for Tabular Engineering Data

Using AI-Enabled Divergence and Convergence Patterns as a Quantitative Artifact in Design Education

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