Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials

11/28/2020
by   Sirui Bi, et al.
0

Topology optimization (TO) is a popular and powerful computational approach for designing novel structures, materials, and devices. Two computational challenges have limited the applicability of TO to a variety of industrial applications. First, a TO problem often involves a large number of design variables to guarantee sufficient expressive power. Second, many TO problems require a large number of expensive physical model simulations, and those simulations cannot be parallelized. To address these issues, we propose a general scalable deep-learning (DL) based TO framework, referred to as SDL-TO, which utilizes parallel schemes in high performance computing (HPC) to accelerate the TO process for designing additively manufactured (AM) materials. Unlike the existing studies of DL for TO, our framework accelerates TO by learning the iterative history data and simultaneously training on the mapping between the given design and its gradient. The surrogate gradient is learned by utilizing parallel computing on multiple CPUs incorporated with a distributed DL training on multiple GPUs. The learned TO gradient enables a fast online update scheme instead of an expensive update based on the physical simulator or solver. Using a local sampling strategy, we achieve to reduce the intrinsic high dimensionality of the design space and improve the training accuracy and the scalability of the SDL-TO framework. The method is demonstrated by benchmark examples and AM materials design for heat conduction. The proposed SDL-TO framework shows competitive performance compared to the baseline methods but significantly reduces the computational cost by a speed up of around 8.6x over the standard TO implementation.

READ FULL TEXT

page 3

page 5

research
07/22/2022

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Graph Convolutional Neural Network (GCNN) is a popular class of deep lea...
research
06/24/2022

DeepAL for Regression Using ε-weighted Hybrid Query Strategy

Designing an inexpensive approximate surrogate model that captures the s...
research
02/27/2022

Deep Learning-Based Inverse Design for Engineering Systems: Multidisciplinary Design Optimization of Automotive Brakes

The braking performance of the brake system is a target performance that...
research
10/31/2017

ChainerMN: Scalable Distributed Deep Learning Framework

One of the keys for deep learning to have made a breakthrough in various...
research
05/27/2023

Python Wrapper for Simulating Multi-Fidelity Optimization on HPO Benchmarks without Any Wait

Hyperparameter (HP) optimization of deep learning (DL) is essential for ...
research
05/26/2021

kEDM: A Performance-portable Implementation of Empirical Dynamic Modeling using Kokkos

Empirical Dynamic Modeling (EDM) is a state-of-the-art non-linear time-s...

Please sign up or login with your details

Forgot password? Click here to reset