12 citations · 12 across the 1 of their papers we have counts for
6 papers
StressGAN: A Generative Deep Learning Model for 2D Stress Distribution Prediction
Haoliang Jiang, Zhenguo Nie, Roselyn Yeo +2
Using deep learning to analyze mechanical stress distributions has been gaining interest with the demand for fast stress analysis methods. Deep learning approaches have achieved ex…
TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain
Zhenguo Nie, Tong Lin, Haoliang Jiang +1
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…
3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders
Wentai Zhang, Zhangsihao Yang, Haoliang Jiang +5
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. The…
3D Conceptual Design Using Deep Learning
Zhangsihao Yang, Haoliang Jiang, Zou Lan
This article proposes a data-driven methodology to achieve a fast design support, in order to generate or develop novel designs covering multiple object categories. This methodolog…
The TauSpinner approach for electroweak corrections in LHC Z to ll observables
E. Richter-Was, Z. Was
The LHC enters era of the Standard Model Z-boson couplings precise measurements, to match precision of LEP. The calculations of electroweak (EW) corrections in the Monte Carlo gene…
Data-driven Upsampling of Point Clouds
Wentai Zhang, Haoliang Jiang, Zhangsihao Yang +3
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. In this pa…