Scientific machine learning
Scientific machine learning uses deep learning to model and forecast physical and dynamical systems. Junaid Farooq’s PhD research in this area produced peer-reviewed work including RefreshNet, published in Nonlinear Dynamics, on forecasting systems that change at several speeds at once.
Publications
RefreshNet: Learning multiscale dynamics through hierarchical refreshing
Nonlinear Dynamics, 2024
Hybrid deep neural network for data-driven missile guidance with maneuvering target
Defence Science Journal, 2023
Multiscale autoencoder-RNN architecture for mitigating error accumulation in long-term forecasting
ICETCI 2023, IEEE, 2023
Environmental Science and Pollution Research, 2022
International Journal of Electrical Power & Energy Systems, 2022
Alexandria Engineering Journal, 2021
Deep learning for self-tuning of control systems
ICETCI 2021, IEEE, 2021
A novel adaptive deep learning model of COVID-19 with focus on mortality reduction strategies
Chaos, Solitons & Fractals, 2020