We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang +4 more
wiley +1 more source
Unraveling Band-Tail Effects on Temperature-Dependent Emission in GaAsBi via Photoluminescence. [PDF]
Yan B +6 more
europepmc +1 more source
Concomitant immunoglobulin A nephropathy and membranous nephropathy with Fabry-like zebra bodies: A case report and literature review. [PDF]
Dai Y, Lu C, Bi G, Zhou G, Wang R.
europepmc +1 more source
A Discussion on the Concept of “Wu” in Wang Bi’s Theory
openaire +1 more source
A Chain-of-thought Reasoning Breast Ultrasound Dataset Covering All Histopathology Categories. [PDF]
Yu H +21 more
europepmc +1 more source
Binary and Ternary Classification Prediction for Breast Cancer and Breast Sclerosing Adenosis With Interpretable Artificial Intelligence From Clinical and Imaging Features: A Retrospective, Diagnostic Accuracy Cohort Study. [PDF]
Qu Y +17 more
europepmc +1 more source
Restructuring-Regulated Bismuth Catalyst Promotes Electrochemical CO<sub>2</sub> Reduction to Formic Acid in Acidic Electrolyte. [PDF]
Chen G +11 more
europepmc +1 more source
Molecular characterization of <i>Moniezia expansa</i> (Rudolphi, 1810) (Cestoda: Anoplocephalidae) from a captive common eland (<i>Tragelaphus oryx</i>) in Taiwan. [PDF]
Hong P +9 more
europepmc +1 more source

