RESEARCH
We are exploring areas at the intersection of Engineering Design, Machine Learning/Artificial Intelligence, Advanced Manufacturing, and Topology Optimization. My specific research topics include Geometric Modeling, Metamaterial Design, Physics-Driven Design, and Data-Driven Design/Simulation/Manufacturing (Inverse Design & Generative Design).Â
We are open to collaborative projects related but not limited to the above areas.
Geometric Modeling & Physics-Driven Design
Topics include cellular structure, computational geometry, FE simulation, design optimization, 3D printing, inverse problem-solving, and generative design.
We provided a set of Generative Design solutions for generating lightweight optimized conformal cellular structures (Meta-Materials) with built-in functional requirements using a traditional iterative design optimization framework.
Related Publications:
Jun Wang and Rahul Rai. "Design and Optimization of Quasi-Periodic Volumetric Microstructures by Aperiodic Tiling and Implicit Function." Proceedings of ASME 2023 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, August 20-23, 2023, Boston, Massachusetts, USA. https://doi.org/10.1115/DETC2023-116668.
Sina Rastegarzadeh, Jida Huang, and Jun Wang. "Architected Cellular Materials for Aerospace Components Design and Manufacturing." Proceedings of ASME 2023 Aerospace Structures, Structural Dynamics, and Materials Conference, June 19-21, 2023, San Diego, CA, USA. https://doi.org/10.1115/SSDM2023-107329.
Sina Rastegarzadeh, Jun Wang, and Jida Huang. "Implicitly Represented Architected Materials for Multi-ScaleDesign and High-Resolution Additive Manufacturing," Advanced MaterialsTechnologies (2023). https://doi.org/10.1002/admt.202300274.
Jun Wang and Jida Huang. "Functionally Graded Non-Periodic Cellular Structure Design and Optimization." Journal of Computing and Information Science in Engineering (2021). https://doi.org/10.1115/1.4053039. [PDF]
Jun Wang and Jida Huang. "Functionally Graded Non-Periodic Cellular Structure Design Using a Surrogate Model-Based Optimization Scheme." Proceedings of ASME 2021 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, August 17-19, 2021, Online, Virtual, USA. (2021 ASME CIE Best Paper Award). https://doi.org/10.1115/DETC2021-71678 [PDF]
Rahul Rai and Jun Wang. "Periodic Cellular Structure Based Design for Additive Manufacturing Approach for Light Weighting and Optimizing Strong Functional Parts." U.S. Patent Application No. 17/141,169. https://patents.google.com/patent/US20210216683A1/en (2021). [PDF]
Jun Wang, Jesse Callanan, Oladapo Ogunbodede, and Rahul Rai. "Hierarchical combinatorial design and optimization of non-periodic metamaterial structures." Additive Manufacturing (2020). https://doi.org/10.1016/j.addma.2020.101710. [PDF]
Jun Wang and Rahul Rai. "Generative design of conformal cubic periodic cellular structures using a surrogate model-based optimisation scheme." International Journal of Production Research (2020). https://doi.org/10.1080/00207543.2020.1859637. [PDF}
"Integrated Framework for Design Exploration and Analysis of Periodic, Non-periodic, and Quasi-periodic Cellular Structures Based Components." Thesis for Ph.D., February 2019. [PDF]Â
Jun Wang, Rahul Rai, and Jason Armstrong. "Investigation of Compressive Deformation Behaviors of Cubic Periodic Cellular Structural Cubes through 3D Printed Parts and FE Simulations". Rapid Prototyping Journal (2019). DOI: 10.1108/RPJ-03-2019-0069. [PDF]
Jesse Callanan, Oladapo Ogunbodede, Maulikkumar Dhameliya, Jun Wang, and Rahul Rai. "Hierarchical Combinatorial Design and Optimization of Quasi-Periodic Metamaterial Structures". Proceedings of ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, August 26-29, 2018, Quebec City, Quebec, Canada. [PDF]
Jun Wang and Rahul Rai. "Classification of Bio-Inspired Periodic Cubic Cellular Materials Based on Compressive Deformation Behaviors of 3D Printed Parts and FE Simulations". Proceedings of ASME 2016 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, August 21-24, 2016, Charlotte, North Carolina, USA. [PDF]
Data-Driven Design (Inverse Design, Generative Design)
Topics include deep generative models (e.g., GANs), geometric modeling, finite-element (FE) simulation, adjoint optimization, shape representation, homogenization, high-performance computing, and topology optimization.
We developed two Inverse Design models for 2D airfoils and cellular structures via the use of Deep Generative Models (i.e., Entropic GAN and Conditional GAN), which short-circuit the costly iteration in traditional derivative designs by constructing a direct mapping from performance/environments to design variables.
Related Publications:
Arthur Drake, Jun Wang, Qiuyi Chen, Ardalan Nejat, James Guest, Mark Fuge. "To Quantize or Not to Quantize: Effects on Generative Models for 2D Heat Sink Design." Proceedings of ASME 2024 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, August 25-28, 2024, Washington, DC, USA. https://doi.org/10.1115/DETC2024-142052Â
Shaoliang Yang, Kang Wang, Jun Wang. "Enhancing Isogeometric Analysis with NURBS-Based Synthesis." Proceedings of ASME 2024 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, August 25-28, 2024, Washington, DC, USA. https://doi.org/10.1115/DETC2024-142195Â
Shaoliang Yang, Jun Wang, and Kang Wang. "NURBS-OT:Â An Advanced Model for Generative Curve Modeling." Journal of Mechanical Design (2024). Â https://doi.org/10.1115/1.4066549
Millad Habibi, Shai Bernard, Jun Wang, and Mark Fuge. "Mean Squared Error May Lead You Astray When Optimizing Your Inverse Design Methods." Journal of Mechanical Design (2024). https://doi.org/10.1115/1.4066102
Jun Wang and Mark Fuge. "Training Efficiency Gains in Data-Driven 2D Airfoil Inverse Design using Active Learning." Proceedings of AIAA SciTech 2024 Forum, January 8-12, 2024, Orlando, Florida, USA. https://arc.aiaa.org/doi/abs/10.2514/6.2024-1229.
Milad Habibi, Jun Wang, Mark Fuge. "When Is it Actually Worth Learning Inverse Design?" Proceedings of ASME 2023 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, August 20-23, 2023, Boston, Massachusetts, USA. https://doi.org/10.1115/DETC2023-116678.
Darshil Patel, Ruoyu Yang, Jun Wang, Rahul Rai, and Gary Gargush. "Deep learning-based inverse design framework for property targeted novel architectured interpenetrating phase composites." Composite Structures (2023). 10.1016/j.compstruct.2023.116783Â
Shai Bernard, Jun Wang, Mark Fuge. "Mean Squared Error May Lead You Astray When Optimizing Your Inverse Design Methods." Proceedings of ASME 2022 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, August 14-17, 2022, St. Louis, Missouri, USA. https://doi.org/10.1115/DETC2022-90065.
Jun Wang, Wei (Wayne) Chen, Daicong Da, Mark Fuge, and Rahul Rai. "IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular Structures." Computer Methods in Applied Mechanics and Engineering (2022). https://authors.elsevier.com/a/1f7TmAQEIzVqB.
Qiuyi Chen, Jun Wang, Phillip Pope, Wei (Wayne) Chen, and Mark Fuge. "Inverse Design of 2D Airfoils using Conditional Generative Models and Surrogate Log-Likelihoods." Journal of Mechanical Design (2021). https://doi.org/10.1115/1.4052846. [PDF]
Jun Wang, Wei Chen, Mark Fuge, and Rahul Rai. "IH-GAN: A Conditional Generative Model for Implicit Surface-Based Inverse Design of Cellular Structures." https://arxiv.org/abs/2103.02588 (2021). [PDF]
Data-Driven Manufacturing & Simulation
Topics include neural networks, finite-element (FE) simulation, 3D printing, shape representation, image processing, fluid mechanics, and geometric deep learning.
I developed Machine Learning (ML)-based prediction models for Advanced Manufacturing processes (e.g., SLA 3D printing) and Fluid Mechanics (e.g., CFD).
Related Publications:
Jun Wang, Sonjoy Das, Rahul Rai, and Chi Zhou. "Data-Driven Simulation for Fast Prediction of Pull-Up Process in Bottom-Up Stereo-lithography". Computer-Aided Design 99 (2018): 29-42. [PDF]
Jun Wang, Sonjoy Das, Chi Zhou, and Rahul Rai. "Data-Driven Simulation for Fast Prediction of Pull-Up Process in Bottom-Up Stereo-Lithography". Proceedings of ASME 2016 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, August 21-24, 2016, Charlotte, North Carolina, USA. [PDF]
Aditya Khadikar, Jun Wang, and Rahul Rai. "Deep Learning-Based Stress Prediction for Bottom-Up SLA 3D Printing Process". The International Journal of Advanced Manufacturing Technology 102, no. 5-8 (2019): 2555-2569. [PDF]
Jun Wang, Kevin Chiu, and Mark Fuge. "Learning to Abstract and Compose Mechanical Device Function and Behavior". Submitted to ASME 2020 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, August 16-19, 2020, St. Louis, Missouri, USA. [PDF]
Topology Optimization
Topics include multi-scale topology optimization, functional graded cellular structural design, machine learning-based topology optimization.
Related Publications:
Sina Rastegarzadeh, Jida Huang, and Jun Wang. "Architected Cellular Materials for Aerospace Components Design and Manufacturing." Proceedings of ASME 2023 Aerospace Structures, Structural Dynamics, and Materials Conference, June 19-21, 2023, San Diego, CA, USA. https://doi.org/10.1115/SSDM2023-107329.
Sina Rastegarzadeh, Jun Wang, and Jida Huang. "Implicitly Represented Architected Materials for Multi-ScaleDesign and High-Resolution Additive Manufacturing," Advanced MaterialsTechnologies (2023). https://doi.org/10.1002/admt.202300274.
Sina Rastegarzadeh, Jun Wang, and Jida Huang. "Multi-Scale Topology Optimization With Neural Network-Assisted Optimizer." Proceedings of ASME 2022 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, August 14-17, 2022, St. Louis, Missouri, USA. https://doi.org/10.1115/DETC2022-89538.
Sina Rastegarzadeh, Jun Wang, and Jida Huang. "Neural Network-Assisted Design: A Study of Multiscale Topology Optimization With Smoothly Graded Cellular Structures ." Journal of Mechanical Design (2022). [PDF].
Sina Rastegarzadeh, Jun Wang, and Jida Huang. "Two-Scale Topology Optimization with Isotropic and Orthotropic Microstructures." Designs (2022). https://www.mdpi.com/2411-9660/6/5/73.
Sina Rastegarzadeh, Jun Wang, and Jida Huang. "Two-Scale Topology Optimization with Parameterized Cellular Structures." Proceedings of ASME 2021 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, August 17-19, 2021, Online, Virtual, USA. https://doi.org/10.1115/DETC2021-71980 [PDF]