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Range-Net: A High Precision Neural SVD
For Big Data applications, computing a rank-$r$ Singular Value Decomposition (SVD) is restrictive due to the main memory requirements.
Learning Quantities of Interest from Dynamical Systems for Observation-Consistent Inversion
Following the introduction and demonstration of LUQ, numerical results from several SIPs are presented for a variety of dynamical systems arising in the life and physical sciences.
Prevention is Better than Cure: Handling Basis Collapse and Transparency in Dense Networks
We demonstrate through carefully chosen numerical experiments that the basis collapse issue leads to the design of massively redundant networks.
TIME: A Transparent, Interpretable, Model-Adaptive and Explainable Neural Network for Dynamic Physical Processes
Partial Differential Equations are infinite dimensional encoded representations of physical processes.
High Temporal Resolution Rainfall Runoff Modelling Using Long-Short-Term-Memory (LSTM) Networks
Accurate and efficient models for rainfall runoff (RR) simulations are crucial for flood risk management.
