Results 201 to 210 of about 33,546 (278)
Chaos analysis and traveling wave solutions for fractional (3+1)-dimensional Wazwaz Kaur Boussinesq equation with beta derivative. [PDF]
Zhao S.
europepmc +1 more source
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
Digital Quantum Simulation of Wavepacket Correlations in a Chemical Reaction. [PDF]
Mohtashim SI, Kais S.
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
Analytical construction and visualization of nonlinear waves in the (2+1) dimensional Kadomtsev-Petviashvili-Sawada-Kotera-Ramani equation with stability analysis. [PDF]
Ahmed M, Ahmed HM, Badra N, Samir I.
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
Quantum avalanches in [Formula: see text]-preserving interacting Ising Majorana chain. [PDF]
Zhang L, Xu K, Fan H.
europepmc +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
An Autonomous Large Language Model‐Agent Framework for Transparent and Local Time Series Forecasting
Architecture of the proposed large language model (LLM)‐based agent framework for autonomous time series forecasting in thermal power generation systems. The framework operates through a vertical pipeline initiated by natural language queries from users, which are processed by the LLM Agent Core powered by Llama.cpp and a ReAct loop with persistent ...
William Gouvêa Buratto +5 more
wiley +1 more source

