466 citations · 1.6k across the 11 of their papers we have counts for
7 papers · 1 filter
Sensitivity-Constrained Neural Operators for Data-Efficient Forward and Inverse Modeling of Partial Differential Equation Systems
Abdolmehdi Behroozi, Chaopeng Shen, Daniel Kifer +1
Neural operators provide fast surrogates for partial differential equation (PDE) solvers, but their reliability can degrade for high-dimensional spatial inputs and inverse or repea…
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Chaopeng Shen, Alison P. Appling, Pierre Gentine +27
Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powe…
Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy
Dapeng Feng, Jiangtao Liu, Kathryn Lawson +1
Predictions of hydrologic variables across the entire water cycle have significant value for water resource management as well as downstream applications such as ecosystem and wate…
The data synergy effects of time-series deep learning models in hydrology
Kuai Fang, Daniel Kifer, Kathryn Lawson +2
When fitting statistical models to variables in geoscientific disciplines such as hydrology, it is a customary practice to regionalize - to divide a large spatial domain into multi…
Continental-scale streamflow modeling of basins with reservoirs: towards a coherent deep-learning-based strategy
Wenyu Ouyang, Kathryn Lawson, Dapeng Feng +3
A large fraction of major waterways have dams influencing streamflow, which must be accounted for in large-scale hydrologic modeling. However, daily streamflow prediction for basin…
Prediction in ungauged regions with sparse flow duration curves and input-selection ensemble modeling
Dapeng Feng, Kathryn Lawson, Chaopeng Shen
While long short-term memory (LSTM) models have demonstrated stellar performance with streamflow predictions, there are major risks in applying these models in contiguous regions w…
From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling
Wen-Ping Tsai, Dapeng Feng, Ming Pan +5
The behaviors and skills of models in many geosciences (e.g., hydrology and ecosystem sciences) strongly depend on spatially-varying parameters that need calibration. A well-calibr…