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09/19/2023
Koopman Invertible Autoencoder: Leveraging Forward and Backward Dynamics for Temporal Modeling
Accurate long-term predictions are the foundations for many machine lear...
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08/18/2023
Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources
Prediction of dynamic environmental variables in unmonitored sites remai...
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06/14/2023
HOSSnet: an Efficient Physics-Guided Neural Network for Simulating Crack Propagation
Hybrid Optimization Software Suite (HOSS), which is a combined finite-di...
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04/24/2023
Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement
Simulating turbulence is critical for many societally important applicat...
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02/16/2023
Entity Aware Modelling: A Survey
Personalized prediction of responses for individual entities caused by e...
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01/20/2023
STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19
Human mobility estimation is crucial during the COVID-19 pandemic due to...
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01/01/2023
Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin
Cashews are grown by over 3 million smallholders in more than 40 countri...
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12/10/2022
Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability
Spatio-temporal machine learning is critically needed for a variety of s...
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08/19/2022
Graph-Augmented Cyclic Learning Framework for Similarity Estimation of Medical Clinical Notes
Semantic textual similarity (STS) in the clinical domain helps improve d...
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02/11/2022
Modeling Reservoir Release Using Pseudo-Prospective Learning and Physical Simulations to Predict Water Temperature
This paper proposes a new data-driven method for predicting water temper...
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10/11/2021
Heterogeneous Stream-reservoir Graph Networks with Data Assimilation
Accurate prediction of water temperature in streams is critical for moni...
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09/06/2021
Reconstructing High-resolution Turbulent Flows Using Physics-Guided Neural Networks
Direct numerical simulation (DNS) of turbulent flows is computationally ...
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08/16/2021
Weakly Supervised Classification Using Group-Level Labels
In many applications, finding adequate labeled data to train predictive ...
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08/16/2021
Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping
Collecting large annotated datasets in Remote Sensing is often expensive...
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07/26/2021
CalCROP21: A Georeferenced multi-spectral dataset of Satellite Imagery and Crop Labels
Mapping and monitoring crops is a key step towards sustainable intensifi...
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05/02/2021
Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping
The availability of massive earth observing satellite data provide huge ...
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03/03/2021
Land Cover Mapping in Limited Labels Scenario: A Survey
Land cover mapping is essential for monitoring global environmental chan...
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11/11/2020
Teaching deep learning causal effects improves predictive performance
Causal inference is a powerful statistical methodology for explanatory a...
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11/10/2020
Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning
Most environmental data come from a minority of well-observed sites. An ...
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10/27/2020
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks
Effective training of advanced ML models requires large amounts of label...
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09/26/2020
Physics-Guided Recurrent Graph Networks for Predicting Flow and Temperature in River Networks
This paper proposes a physics-guided machine learning approach that comb...
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03/10/2020
Integrating Physics-Based Modeling with Machine Learning: A Survey
In this manuscript, we provide a structured and comprehensive overview o...
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01/28/2020
Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles
Physics-based models of dynamical systems are often used to study engine...
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01/03/2020
Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data
Many real-world phenomena are observed at multiple resolutions. Predicti...
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04/08/2019
Automated Monitoring Cropland Using Remote Sensing Data: Challenges and Opportunities for Machine Learning
This paper provides an overview of how recent advances in machine learni...
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10/31/2018
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 (PGR...
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10/05/2018
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 k...
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12/19/2017