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A Survey on Solving and Discovering Differential Equations Using Deep Neural Networks
Ordinary and partial differential equations (DE) are used extensively in scientific and mathematical domains to model physical systems.
SAMCNet for Spatial-configuration-based Classification: A Summary of Results
This problem is challenging due to an exponential number of category subsets which may vary in the strength of spatial interactions.
Towards Comparative Physical Interpretation of Spatial Variability Aware Neural Networks: A Summary of Results
The proposed physical interpretation improves the transparency of SVANN models and the analytical results highlight the trade-off between model transparency and model performance (e. g., F1-score).
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