人工神经网络
试验装置
深度学习
人工智能
氮化镓
试验数据
计算机科学
晶体管
一致性(知识库)
机器学习
集合(抽象数据类型)
功能(生物学)
材料科学
工程类
纳米技术
电气工程
生物
进化生物学
电压
程序设计语言
图层(电子)
作者
Shivanshu Mishra,Bipin Gaikwad,Nidhi Chaturvedi
摘要
This research article proposes a deep learning framework that addresses two major hurdles in adopting deep learning techniques for solving physics-based problems. One is the requirement of a large data set for training the deep learning (DL) model and another is the consistency of a DL model with the physics of a phenomenon. The framework is generic that can be applied to model a phenomenon in physics if its behavior is known. A semi-supervised physics guided neural network (SPGNN) has been developed based on our framework to demonstrate the concept. SPGNN models the I–V characteristics of gallium nitride based high electron mobility transistors (GaN HEMTs). A two-stage method has been proposed to train a DL model. In the first stage, the DL model is trained via an unsupervised learning method using the analytical physics-based model of a field-effect transistor (FET) as a loss function of the DL model that incorporates the physics of the FET in the DL model. Later, the DL model is fine-tuned with a small set of experimental data in the second stage. Performance of SPGNN has been assessed on various sizes of the data set with 100, 500, 1000, 1500, and 2000 samples. SPGNN significantly reduces the training data requirement by more than 80% and provides better performance than a traditionally trained neural network (TTNN), even for the unseen test data set. SPGNN predicts 32.4% of the unseen test data with less than 1% of error and only 0.4% of the unseen test data with more than 10% of error.
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