311 citations · 497 across the 9 of their papers we have counts for
9 papers
Heterogeneous Stream-reservoir Graph Networks with Data Assimilation
Shengyu Chen, Alison Appling, Samantha Oliver +5
Accurate prediction of water temperature in streams is critical for monitoring and understanding biogeochemical and ecological processes in streams. Stream temperature is affected…
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…
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks
Xiaowei Jia, Beiyu Lin, Jacob Zwart +4
Effective training of advanced ML models requires large amounts of labeled data, which is often scarce in scientific problems given the substantial human labor and material cost to…
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…
Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles
Xiaowei Jia, Jared Willard, Anuj Karpatne +4
Physics-based models of dynamical systems are often used to study engineering and environmental systems. Despite their extensive use, these models have several well-known limitatio…
Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling
Arka Daw, R. Quinn Thomas, Cayelan C. Carey +3
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 know…
Physics Guided Recurrent Neural Networks For Modeling Dynamical Systems: Application to Monitoring Water Temperature And Quality In Lakes
Xiaowei Jia, Anuj Karpatne, Jared Willard +5
In this paper, we introduce a novel framework for combining scientific knowledge within physics-based models and recurrent neural networks to advance scientific discovery in many d…
Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles
Xiaowei Jia, Jared Willard, Anuj Karpatne +4
This paper proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improve the mo…
Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling
Arka Daw, Anuj Karpatne, William Watkins +2
This paper introduces a framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery. This framework, termed physics-gu…