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Found 9 papers, 3 papers with code
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and ushering in a paradigm shift.
S3E: A Large-scale Multimodal Dataset for Collaborative SLAM
With the advanced request to employ a team of robots to perform a task collaboratively, the research community has become increasingly interested in collaborative simultaneous localization and mapping.
Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy
Without using an ensemble or post-processor, {\delta} models can obtain a median Nash Sutcliffe efficiency of 0. 732 for 671 basins across the USA for the Daymet forcing dataset, compared to 0. 748 from a state-of-the-art LSTM model with the same setup.
Continental-scale streamflow modeling of basins with reservoirs: towards a coherent deep-learning-based strategy
However, dammed basins must be present in the training dataset.
The data synergy effects of time-series deep learning models in hydrology
We hypothesize that DL models automatically adjust their internal representations to identify commonalities while also providing sufficient discriminatory information to the model.
Exploring Geometry-Aware Contrast and Clustering Harmonization for Self-Supervised 3D Object Detection
Here we present a novel self-supervised 3D Object detection framework that seamlessly integrates the geometry-aware contrast and clustering harmonization to lift the unsupervised 3D representation learning, named GCC-3D.
Prediction in ungauged regions with sparse flow duration curves and input-selection ensemble modeling
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 with no gauges, or predictions in ungauged regions (PUR) problems.
From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling
The behaviors and skills of models in many geosciences (e. g., hydrology and ecosystem sciences) strongly depend on spatially-varying parameters that need calibration.
Enhancing streamflow forecast and extracting insights using long-short term memory networks with data integration at continental scales
DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater-dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions.
