1.7k citations · 2.4k across the 28 of their papers we have counts for
7 papers · 1 filter
Physics Guided Machine Learning Methods for Hydrology
Ankush Khandelwal, Shaoming Xu, Xiang Li +5
Streamflow prediction is one of the key challenges in the field of hydrology due to the complex interplay between multiple non-linear physical mechanisms behind streamflow generati…
Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning
Jared D. Willard, Jordan S. Read, Alison P. Appling +3
Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitor…
Physics-Guided Recurrent Graph Networks for Predicting Flow and Temperature in River Networks
Xiaowei Jia, Jacob Zwart, Jeffrey Sadler +8
This paper proposes a physics-guided machine learning approach that combines advanced machine learning models and physics-based models to improve the prediction of water flow and t…
Inverse Problems, Deep Learning, and Symmetry Breaking
Kshitij Tayal, Chieh-Hsin Lai, Vipin Kumar +1
In many physical systems, inputs related by intrinsic system symmetries are mapped to the same output. When inverting such systems, i.e., solving the associated inverse problems, t…
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
Jared Willard, Xiaowei Jia, Shaoming Xu +2
There is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling ap…
Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data
Guruprasad Nayak, Rahul Ghosh, Xiaowei Jia +2
Many real-world phenomena are observed at multiple resolutions. Predictive models designed to predict these phenomena typically consider different resolutions separately. This appr…