Corpus ID: 215744868

Deep learning-based topological optimization for representing a user-specified design area

  title={Deep learning-based topological optimization for representing a user-specified design area},
  author={Keigo Nakamura and Yoshiro Suzuki},
Presently, topology optimization requires multiple iterations to create an optimized structure for given conditions. Among the conditions for topology optimization,the design area is one of the most important for structural design. In this study, we propose a new deep learning model to generate an optimized structure for a given design domain and other boundary conditions without iteration. For this purpose, we used open-source topology optimization MATLAB code to generate a pair of optimized… Expand
1 Citations
Two-stage convolutional encoder-decoder network to improve the performance and reliability of deep learning models for topology optimization
A vital necessity when employing state-of-the-art deep neural networks (DNNs) for topology optimization is to predict near-optimal structures while satisfying pre-defined optimization constraints andExpand


Deep learning for determining a near-optimal topological design without any iteration
The performance evaluation results of the integrated network demonstrate that the proposed method can determine a near-optimal structure in terms of pixel values and compliance with negligible computational time. Expand
A novel topology design approach using an integrated deep learning network architecture
This work presents a new topology design procedure to generate optimal structures using an integrated Generative Adversarial Networks (GANs) and convolutional neural network architecture. Expand
Neural networks for topology optimization
This research introduces convolutional encoder-decoder architecture and the overall approach of solving the layout problem with high performance and demonstrates the ability of the application of the proposed model to other problems. Expand
Deep Fluids: A Generative Network for Parameterized Fluid Simulations
The proposed generative model is optimized for fluids by a novel loss function that guarantees divergence‐free velocity fields at all times, thus enabling applications such as fast construction of simulations, interpolation of fluids with different parameters, time re‐sampling, latent space simulations, and compression of fluid simulation data. Expand
Adam: A Method for Stochastic Optimization
This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Expand
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Expand
Semantic Image Synthesis With Spatially-Adaptive Normalization
S spatially-adaptive normalization is proposed, a simple but effective layer for synthesizing photorealistic images given an input semantic layout that allows users to easily control the style and content of image synthesis results as well as create multi-modal results. Expand
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
A new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs) is presented, which significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing. Expand
Material interpolation schemes in topology optimization
Summary In topology optimization of structures, materials and mechanisms, parametrization of geometry is often performed by a grey-scale density-like interpolation function. In this paper we analyzeExpand
Despite their recent successes, GAN models for semantic image synthesis still suffer from poor image quality when trained with only adversarial supervision. Historically, additionally employing theExpand