Vinh Tran Georgia Tech
Wang began at Georgia Tech in Fall 2009 as an assistant professor. Wang's research is in the areas of design, manufacturing, and Integrated computational materials engineering. He is interested in computer-aided design, geometric modeling and processing, computer-aided manufacturing, multiscale simulation, and uncertainty quantification.Currently, Dr. Wang studies integrated product-materials design and manufacturing process design, where process-structure-property relationships are established with physics-based data-driven approaches for design optimization. The Multiscale Systems Engineering research group led by him develops new methodologies and computational schemes to solve the technical challenges of high dimensionality, high complexity, and uncertainty associated with product, process, and systems design at multiple length and time scales.Computational design tools for multiscale systems with sizes ranging from nanometers to kilometers will be indispensable for engineers' daily work in the near future.
The research mission of the Multiscale Systems Engineering group is to create new modeling and simulation mechanisms and tools with underlying scientific rigor that are suitable for multiscale systems engineering for better and faster product innovation. Our education mission is to train engineers of the future to gain necessary knowledge as well as analytical, computational, communication, and self-learning skills for future work in a collaborative environment as knowledge creators and integrators. Representative PublicationsComputational Materials Design.
Wang Y. (2007) Periodic surface modeling for computer aided nano design. Computer-Aided Design, 39(3): 179-189. Wang Y. And Rosen D.W. (2010) Multiscale heterogeneous modeling with surfacelets. Computer-Aided Design & Applications, 7(5): 759-776.
Huang W., Wang Y., and Rosen D.W. (2014) Inverse surfacelet transform for image reconstruction with constrained-conjugate gradient methods. Journal of Computing and Information Science in Engineering, 14(2): 021005. Huang W., Didari S, Wang Y., and Harris T.A.L. (2015) Generalized periodic surface model and its application in designing fibrous porous media. Engineering Computations, 32(1): 7-36. Tran A.V., He L., and Wang Y.
(2018) An efficient first-principles saddle point searching method based on distributed kriging metamodels. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B, 4(1): 011006.Manufacturing Process Modeling and Monitoring. Wang Y. (2008) Semantic tolerance modeling with generalized intervals.
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Journal of Mechanical Design, 130(8): 081701(1-7). Wu H., Wang Y., and Yu Z. (2016) In-situ monitoring of FDM machine condition via acoustic emission. International Journal of Advanced Manufacturing Technology, 84(5): 1483-1495.
Wang Y. (2016) Controlled kinetic Monte Carlo simulation for computer-aided nanomanufacturing. Journal of Micro and Nano-Manufacturing, 4(1): 011001. Lu Y.
(2018) Monitoring temperature in additive manufacturing with physics-based compressive sensing. Journal of Manufacturing Systems, 48(Part C): 60-70. Liu J., Hu Y., Wang Y., Wu B., Fan J., and Hu Z. (2018) An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis.
Vinh Tran Georgia Tech College
Measurement Science & Technology, 29(5): 055103. Liu D. (2019) Mesoscale multi-physics simulation of rapid solidification of Ti-6Al-4V alloy. Additive Manufacturing, 25: 551-562.Uncertainty Quantification and Bayesian Optimization. Wang Y. (2010) Imprecise probabilities based on generalised intervals for system reliability assessment.
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International Journal of Reliability & Safety, 4(4): 319-342. Wang Y. (2011) Multiscale uncertainty quantification based on a generalized hidden Markov model. Journal of Mechanical Design, 133(3): 031004.
Wang Y. (2013) Reliable kinetic Monte Carlo simulation based on random set sampling. Soft Computing, 17(8): 1439-1451. Hu J., Wang Y., Cheng A., and Zhong Z.
(2015) Sensitivity analysis for quantified interval constraints problems. Journal of Mechanical Design, 137(4): 041701. Wang Y. (2016) Model form calibration in drift-diffusion simulation using fractional derivatives. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B, 2(3): 031006. Tran A.V. (2017) Reliable molecular dynamics: Uncertainty quantification using interval analysis in molecular dynamics simulation.
Computational Materials Science, 127: 141-160. Tran A.V., Sun J., Furlan J.M., Pagalthivarthi K.V., Visintainer R.J., and Wang Y. (2019) pBO-2GP-3B: A batch parallel known/unknown constrained Bayesian optimization with feasibility classification and its applications in computational fluid dynamics.
Computer Methods in Applied Mechanics and Engineering, 347: 827-852. Tran A.V., Tran M.N., and Wang Y. (2019) Constrained mixed integer Gaussian mixture Bayesian optimization and its applications in designing fractal and auxetic metamaterials. Structural & Multidisciplinary Optimization, (in press)Design Informatics and Design Space Exploration. Wang Y.
And Nnaji B.O. (2005) Geometry-based semantic ID for persistent and interoperable reference in feature-based parametric modeling. Computer-Aided Design, 37(10): 1081-1093. Wang Y., Ajoku P.N., Brustoloni J.C., and Nnaji B.O. (2006) Intellectual property protection in collaborative design through lean information modeling and sharing.
Journal of Computing and Information Science in Engineering, 6(2): 149-159. Tessier S.M. (2013) Ontology-based feature mapping and verification between CAD systems. Advanced Engineering Informatics, 27(1): 76-92. Hu J., Aminzadeh M., and Wang Y.
(2014) Searching feasible design space by solving quantified constraint satisfaction problems. Journal of Mechanical Design, 136(3): 031002.Cyber-Physical Systems. Wang Y. (2018) Trust quantification for networked cyber-physical systems. IEEE Internet of Things Journal, 5(3): 2055-2070. Wang Y. (2018) Resilience quantification for probabilistic design of cyber-physical system networks.
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B, 4(3): 031006.
Ben Wang is the Eugene C. Chair in Manufacturing Systems and Professor in the Stewart School of Industrial & Systems Engineering, and Professor in the School of Materials Science and Engineering at Georgia Tech. In addition, Dr. Wang jointly serves as the executive director of the Georgia Tech Manufacturing Institute.Dr. Wang's primary research interest is in applying emerging technologies to improve manufacturing competitiveness. He specializes in process development for affordable composite materials.
Wang is widely acknowledged as a pioneer in the growing field of nanomaterials science. His main area of research involves a material known as 'buckypaper', which has shown promise in a variety of applications, including the development of aerospace structures, improvements in energy and power efficiency, enhancements in thermal management of engineering systems, and construction of the next-generation of computer displays.Dr. Wang serves on the National Materials and Manufacturing Board (NMMB). NMMB is the principal forum at the U.S.
National Academies for issues related to innovative materials and advanced manufacturing, and has oversight responsibility for National Research Council activities in these technology areas. Wang is a Fellow of the Institute of Industrial Engineers, the Society of Manufacturing Engineers, and the Society for the Advancement of Material and Process Engineering.Because of his contributions to advanced manufacturing and materials, Dr. Wang was invited to deliver a presentation to the U.S. National Research Council Review Panel in support of the U.S. National Nanotechnology Initiative in 2005. In 2012, he was invited to give testimony before the National Academies Committee on Manufacturing Extension Partnership.
In 2012 he was invited to participate in the Roundtable on Strengthening U.S. Advanced Manufacturing in Clean Energy in the White House.In addition to authoring or co-authoring more than 240 refereed journal papers, he is a co-author of three books: Computer-Aided Manufacturing (Prentice-Hall, 1st Edition, 2nd Edition, and 3rd Edition), Computer-Aided Process Planning (Elsevier Science Publishers), and Computer Aided Manufacturing PC Application Software (Delmar Publishers).Dr. Wang earned his bachelor's in industrial engineering from Tunghai University in Taiwan, and his master's in industrial engineering and Ph.D. From Pennsylvania State University. Fellow, Society for the Advancement of Material and Process Engineering 2011. Distinguished Research Professor Award 2011. David F.