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01/10/2023
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Process-Based Modeling (PBM) and Machine Learning (ML) are often perceiv...
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10/25/2022
S3E: A Large-scale Multimodal Dataset for Collaborative SLAM
With the advanced request to employ a team of robots to perform a task c...
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03/28/2022
Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy
Predictions of hydrologic variables across the entire water cycle have s...
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01/12/2021
Continental-scale streamflow modeling of basins with reservoirs: a demonstration of effectiveness and a delineation of challenges
A large fraction of major waterways have dams influencing streamflow, wh...
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01/06/2021
The data synergy effects of time-series deep learning models in hydrology
When fitting statistical models to variables in geoscientific discipline...
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11/26/2020
Prediction in ungauged regions with sparse flow duration curves and input-selection ensemble modeling
While long short-term memory (LSTM) models have demonstrated stellar per...
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07/30/2020
From parameter calibration to parameter learning: Revolutionizing large-scale geoscientific modeling with big data
The behaviors and skills of models in many geoscientific domains strongl...
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12/18/2019