Differentiable modeling to unify machine learning and physical models and advance Geosciences

no code implementations • 10 Jan 2023 • Chaopeng Shen, Alison P. Appling, Pierre Gentine, Toshiyuki Bandai, Hoshin Gupta, Alexandre Tartakovsky, Marco Baity-Jesi, Fabrizio Fenicia, Daniel Kifer, Li Li, Xiaofeng Liu, Wei Ren, Yi Zheng, Ciaran J. Harman, Martyn Clark, Matthew Farthing, Dapeng Feng, Praveen Kumar, Doaa Aboelyazeed, Farshid Rahmani, Hylke E. Beck, Tadd Bindas, Dipankar Dwivedi, Kuai Fang, Marvin Höge, Chris Rackauckas, Tirthankar Roy, Chonggang Xu, Binayak Mohanty, Kathryn Lawson

Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and ushering in a paradigm shift.

Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning

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.

Meta-Learning Transfer Learning

Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling

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.

Uncertainty Quantification