Happy new year and best wishes for 2021!
We start this year with a thematic session of TOP Webinar on “Data-driven Approaches in Topology Optimization” (DATO). It is scheduled for January 26, 2021, Tuesday. 16:00 – 17:30 pm CET, (i.e., 9:00 – 10:30 am Chicago, 10:00 – 11:30 am New York, 7:00 – 8:30 am Los Angeles, 23:00 – 01:30 am Beijing, Jan. 27, 2:00 – 3:30 am Sydney).
1 – Ying Zhou, Queensland University of Technology, Australia
A new data-driven topology optimization framework for structural optimization.
Ying Zhou, Haifei Zhan, Weihong Zhang*, Jihong Zhu, Jinshuai Bai, Qingxia Wang, Yuantong Gu*. Computers & Structures (2020), 239, 106310.
2 – Yongmin Liu, Northeastern University, USA
Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi-Supervised Learning Strategy.
W Ma, Feng Cheng, Yihao Xu, Qinlong Wen, and Yongmin Liu*.Advanced Materials (2019), 31 (39), 1901111.
3 – Glaucio Paulino, Georgia Institute of Technology, USA
Universal machine learning for topology optimization.
Heng Chi, Yuyu Zhang, Tsz Ling Elaine Tang, Lucia Mirabella, Livio Dalloro, Le Song, Glaucio H. Paulino*. Computer Methods in Applied Mechanics and Engineering (2021), to appear.
4 – Stefano Zapperi, University of Milan, Italy
Automatic design of mechanical metamaterial actuators.
Bonfanti, Silvia, Roberto Guerra, Francesc Font Clos, Daniel Rayneau-Kirkhope, and Stefano Zapperi*. Nature Communications (2020), 11, 4162.
5 – Liwei Wang, Northwestern University, USA
Deep generative modeling for mechanistic-based learning and design of metamaterial systems.
Liwei Wang, Yu-Chin Chan, Faez Ahmed, Zhao Liu, Ping Zhu, Wei Chen*. Computer Methods in Applied Mechanics and Engineering(2020), 372, 113377. https://doi.org/10.1016/j.cma.2020.113377