Submission Deadline: 01 August 2018 (closed) View: 154
Gregory J Wagner (gregory.wagner@northwestern.edu)
Greg Wagner received his Ph.D. in Mechanical Engineering from Northwestern University in 2001. He spent over 12 years as a staff member and later manager in the Thermal/Fluid Science and Engineering department at Sandia National Laboratories in Livermore, CA, where his work included multiscale and multiphysics computational methods, multiphase and particulate flow simulation, extended timescale methods for atomistic simulation, and large-scale engineering code development. In January 2015 he joined the faculty of the Mechanical Engineering department at Northwestern. His current research focuses on applying novel simulation methods and high performance computing to multiscale and multiphysics problems, including additive manufacturing in metals, environmental transport, and multiphase flows.
WaiChing Sun (wsun@columbia.edu)
WaiChing Sun works in theoretical and computational mechanics for porous and geological materials. He obtained his B.S. from UC Davis (2005); M.S. (geomechanics) from Stanford (2007); M.A. degree from Princeton (2008); and Ph.D. in theoretical and applied mechanics from Northwestern (2011). Prior to joining Columbia, he was a senior member of technical staff in the mechanics of materials department at Sandia National Laboratories (Livermore, CA). He is the recipient of the Zienkiewicz Numerical Methods in Engineering Prize in 2016, US Air Force Young Investigator Program Award in 2017, Dresden Fellowship in 2016, US Army Young Investigator Program Award in 2015, and the Caterpillar Best Paper Prize in 2013, among others.
Miguel Bessa (M.A.Bessa@tudelft.nl)
Miguel Bessa's research involves understanding and modeling materials at every scale in a unique experimentally-validated and self-consistent computational framework. He envisions a new era of data-driven design of materials and structures based on Physics-informed machine learning, reduced order models and genetic optimization. Miguel started as an Assistant Professor in Materials Science at the Delft University of Technology on August 2017. He received his PhD in Mechanical Engineering from Northwestern University in September 2016 (Fulbright scholar; 4.0 GPA), and was a postdoctoral scholar in Aerospace at the California Institute of Technology until August 2017.
In recent years, the amount of digital data collectively generated by humans has doubled every two years or less. In the field of computational mechanics, not only is the amount of data increasing, but the form of that data has become increasingly diverse, due in part to technology advancements in sensors, micro-CT imaging, and high-speed cameras. However, making use of these increasingly complex and rich data to enhance scientific and engineering predictions remains a challenging problem. The objective of this special issue is to provide a platform to exchange ideas and advance knowledge on data-driven computational modeling and simulations. We are particularly interested in new ideas that integrate big data analytics and machine learning with existing human knowledge to create, calibrate, verify, and validate forward prediction models as well as inverse problems. Potential topics may include, but are not limited to:
• Reduced-order models and other methods to accelerate data generation and collection
• Data clustering, fusion, mining and feature extraction for computational mechanics
• Uncertainty quantification in the context of Big Data
• Applications of machine/deep learning for constitutive laws and model discovery
• Applications of machine/deep learning to design materials or structures
• Solutions to inverse problems
• Data-driven closure models for sub-grid scales, such as turbulence
• Issues related to the training, verification and validation of data-driven models
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