Search Results for author: Haoliang Jiang

Found 5 papers, 2 papers with code

TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain

1 code implementation5 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.

Generative Adversarial Network

3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders

no code implementations16 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.

3D Shape Representation Decoder

Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks

1 code implementation27 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.

3D Conceptual Design Using Deep Learning

no code implementations5 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.

Data-driven Upsampling of Point Clouds

no code implementations8 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.

Cannot find the paper you are looking for? You can Submit a new open access paper.