1 code implementation • 5 Mar 2020 • Zhenguo Nie, Tong Lin, Haoliang Jiang, Levent Burak Kara
In topology optimization using deep learning, load and boundary conditions represented as vectors or sparse matrices often miss the opportunity to encode a rich view of the design problem, leading to less than ideal generalization results.
no code implementations • 16 Apr 2019 • Wentai Zhang, Zhangsihao Yang, Haoliang Jiang, Suyash Nigam, Soji Yamakawa, Tomotake Furuhata, Kenji Shimada, Levent Burak Kara
We propose a data-driven 3D shape design method that can learn a generative model from a corpus of existing designs, and use this model to produce a wide range of new designs.
1 code implementation • 27 Aug 2018 • Zhenguo Nie, Haoliang Jiang, Levent Burak Kara
The demand for fast and accurate structural analysis is becoming increasingly more prevalent with the advance of generative design and topology optimization technologies.
no code implementations • 5 Aug 2018 • Zhangsihao Yang, Haoliang Jiang, Zou Lan
Through this project, we expect the output can show a clear and smooth interpretation of model from different categories to develop a fast design support to generate novel shapes.
no code implementations • 8 Jul 2018 • Wentai Zhang, Haoliang Jiang, Zhangsihao Yang, Soji Yamakawa, Kenji Shimada, Levent Burak Kara
High quality upsampling of sparse 3D point clouds is critically useful for a wide range of geometric operations such as reconstruction, rendering, meshing, and analysis.