Brief

Researchers have developed ShipNet, a geometric deep learning model designed to predict ship hydrodynamics in real-time. This surrogate model uses hull geometry and speed to approximate pressure distributions and wave patterns, offering a significant speedup over traditional computational fluid dynamics methods. ShipNet achieved high accuracy on a held-out test set, predicting hull pressure with an R^2 of 0.98 and wave fields with an R^2 of 0.91, with inference taking approximately 0.15 seconds per case. AI

Researchers have successfully reproduced and extended MNet, a hybrid 2D/3D convolutional network for medical image segmentation. The study verified MNet's performance on prostate MRI and liver CT datasets, achieving high Dice similarity coefficients. Two new extensions were introduced: a learned Fusion Gating mechanism for adaptive feature blending and a VMamba module for improved long-range modeling, both of which maintained robustness to anisotropic conditions. AI