| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
AI for Big Data Problems in Imaging, Modeling and Synthesis
|
| Presentation Title |
Inverse Design of Porous Structures by Deep Learning and TPU-based Computing |
| Author(s) |
Yuhai Li, Yuhan Liu, Mathieu Bauchy |
| On-Site Speaker (Planned) |
Mathieu Bauchy |
| Abstract Scope |
Although simulations offer a convenient pathway to predict the properties of a given structure, “inverse design” optimizations (i.e., predicting which structure exhibits the most desirable properties) are notoriously challenging problems due to the vastness of the design space. Here, we present a deep learning framework that greatly accelerates the discovery of promising structures featuring optimal mechanical properties. Our approach relies on a convolutional neural network (CNN) model (trained from hight-hroughput peridynamic simulations) that successfully maps a structure to its associated stress-strain curve upon tensile fracture. The CNN predictor is then used to train an inverse CNN generator model enabling the prediction of optimal structures. As a key enabler of this approach, we adopt Tensor Processing Unit (TPU) computing, which offers unprecedented performance in training large, complex neural networks. We suggest that TPU-based deep learning offers a new pathway to accelerate the discovery of novel materials with exotic properties and functionalities. |