Search Results for author:
Found 6 papers, 1 papers with code
Heterogeneous Stream-reservoir Graph Networks with Data Assimilation
In this paper, we propose a heterogeneous recurrent graph model to represent these interacting processes that underlie stream-reservoir networks and improve the prediction of water temperature in all river segments within a network.
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks
In this paper, we develop a real-time active learning method that uses the spatial and temporal contextual information to select representative query samples in a reinforcement learning framework.
Physics-Guided Recurrent Graph Networks for Predicting Flow and Temperature in River Networks
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 temperature in river networks.
Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles
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 modeling of physical processes.
Physics Guided Recurrent Neural Networks For Modeling Dynamical Systems: Application to Monitoring Water Temperature And Quality In Lakes
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 dynamical systems.
Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling
This paper introduces a framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery.
