AutoPINN: When AutoML Meets Physics-Informed Neural Networks

no code implementations • 8 Dec 2022 • Xinle Wu, Dalin Zhang, Miao Zhang, Chenjuan Guo, Shuai Zhao, Yi Zhang, Huai Wang, Bin Yang

We then propose a resource-aware search strategy to explore the search space to find the best PINN model under different resource constraints.

AutoML

Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting

no code implementations • 29 Nov 2022 • Xinle Wu, Dalin Zhang, Miao Zhang, Chenjuan Guo, Bin Yang, Christian S. Jensen

To overcome these limitations, we propose SEARCH, a joint, scalable framework, to automatically devise effective CTS forecasting models.

Correlated Time Series Forecasting Time Series

AutoCTS: Automated Correlated Time Series Forecasting -- Extended Version

no code implementations • 21 Dec 2021 • Xinle Wu, Dalin Zhang, Chenjuan Guo, Chaoyang He, Bin Yang, Christian S. Jensen

Specifically, we design both a micro and a macro search space to model possible architectures of ST-blocks and the connections among heterogeneous ST-blocks, and we provide a search strategy that is able to jointly explore the search spaces to identify optimal forecasting models.

Correlated Time Series Forecasting Time Series