Results 261 to 270 of about 194,210 (329)
Existence and uniqueness of solutions for fuzzy fractional integro-differential equations with boundary conditions. [PDF]
K A, V P, Kausar N, Salman MA.
europepmc +1 more source
Approximation by orthonormal polynomials associated with even exponential weights [PDF]
Bastien Grosse
openalex
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley +1 more source
An innovative meshless approach for solving 2D Allen-Cahn equations using the RBF-compact finite difference method. [PDF]
Fardi M, Azarnavid B, Emami H.
europepmc +1 more source
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
A novel spectral transformation technique based on special functions for improved chest X-ray image classification. [PDF]
Aljohani A.
europepmc +1 more source
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
Estimation of Density Distribution in a Rigid PU Foam Block Manufactured in a Sealed Mold. [PDF]
Beverte I, Cabulis U, Andersons J.
europepmc +1 more source
Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source

