A locally adaptive non-hydrostatic extension to shallow water equations reduces computational cost by about 40% in tsunami scenarios by applying corrections only where indicated by depth and velocity metrics.
Validation and inter-comparison of models for landslide tsunami generation.Ocean Modelling2022; 170: 101943
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RACT is a retrieval-augmented self-supervised method that improves multi-table schema matching precision and completeness by up to 70% by probabilistically retrieving relevant tables to limit column candidate search space.
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Two-Dimensional Locally Adaptive Non-Hydrostatic Extension of Shallow Water Equations
A locally adaptive non-hydrostatic extension to shallow water equations reduces computational cost by about 40% in tsunami scenarios by applying corrections only where indicated by depth and velocity metrics.
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RACT: Retrieval Augmented Column-Table Learning and Prediction for Multi-Table Schema Matching
RACT is a retrieval-augmented self-supervised method that improves multi-table schema matching precision and completeness by up to 70% by probabilistically retrieving relevant tables to limit column candidate search space.