1 code implementation • 10 Nov 2020 • Jared D. Willard, Jordan S. Read, Alison P. Appling, Samantha K. Oliver, Xiaowei Jia, Vipin Kumar
This method, Meta Transfer Learning (MTL), builds a meta-learning model to predict transfer performance from candidate source models to targets using lake attributes and candidates' past performance.
no code implementations • 28 Jan 2020 • Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan S. Read, Jacob A Zwart, Michael Steinbach, Vipin Kumar
Specifically, we show that a PGRNN can improve prediction accuracy over that of physics-based models, while generating outputs consistent with physical laws.
1 code implementation • 6 Nov 2019 • Arka Daw, R. Quinn Thomas, Cayelan C. Carey, Jordan S. Read, Alison P. Appling, Anuj Karpatne
To simultaneously address the rising need of expressing uncertainties in deep learning models along with producing model outputs which are consistent with the known scientific knowledge, we propose a novel physics-guided architecture (PGA) of neural networks in the context of lake temperature modeling where the physical constraints are hard coded in the neural network architecture.