One-Shot Optimal Topology Generation through Theory-Driven Machine Learning

07/27/2018
by   Ruijin Cang, et al.
0

We introduce a theory-driven mechanism for learning a neural network model that performs generative topology design in one shot given a problem setting, circumventing the conventional iterative procedure that computational design tasks usually entail. The proposed mechanism can lead to machines that quickly response to new design requirements based on its knowledge accumulated through past experiences of design generation. Achieving such a mechanism through supervised learning would require an impractically large amount of problem-solution pairs for training, due to the known limitation of deep neural networks in knowledge generalization. To this end, we introduce an interaction between a student (the neural network) and a teacher (the optimality conditions underlying topology optimization): The student learns from existing data and is tested on unseen problems. Deviation of the student's solutions from the optimality conditions is quantified, and used to choose new data points for the student to learn from. We show through a compliance minimization problem that the proposed learning mechanism is significantly more data efficient than using a static dataset under the same computational budget.

READ FULL TEXT

page 8

page 11

page 12

research
01/27/2023

Improved knowledge distillation by utilizing backward pass knowledge in neural networks

Knowledge distillation (KD) is one of the prominent techniques for model...
research
10/26/2017

Knowledge Projection for Deep Neural Networks

While deeper and wider neural networks are actively pushing the performa...
research
07/09/2021

Lifelong Teacher-Student Network Learning

A unique cognitive capability of humans consists in their ability to acq...
research
12/21/2022

Incremental Learning for Neural Radiance Field with Uncertainty-Filtered Knowledge Distillation

Recent neural radiance field (NeRF) representation has achieved great su...
research
04/07/2021

Distilling and Transferring Knowledge via cGAN-generated Samples for Image Classification and Regression

Knowledge distillation (KD) has been actively studied for image classifi...
research
03/23/2021

Teacher-Explorer-Student Learning: A Novel Learning Method for Open Set Recognition

If an unknown example that is not seen during training appears, most rec...
research
02/04/2020

Self-Directed Online Machine Learning for Topology Optimization

Topology optimization by optimally distributing materials in a given dom...

Please sign up or login with your details

Forgot password? Click here to reset