Publications, Activities, and Awards
- Transient Modeling of a Solid Oxide Fuel Cell using an Efficient Deep Learning HY-CNN-NARX Paradigm
- A New Architecture based on Temporal Convolution and Nonlinear Autoregressive Exogenous for Performance Prediction of Solid Oxide Fuel Cells under Dynamic Operation
- Control-oriented Modeling of a Solid Oxide Fuel Cell Affected by Redox Cycling using a Novel Deep Learning Approach
- Control-oriented Modeling of a Solid Oxide Fuel Cell Affected by Redox Cycling using a Novel Deep Learning Approach
- Developing an Efficient Model for a SOFC System Using Self-supervised Convolutional Autoencoder and Stateful LSTM Network
- Performance Prediction of a Range of Diverse Solid Oxide Fuel Cells using Deep Learning and Principal Component Analysis
- SOFC Database