Results 151 to 160 of about 313,686 (220)

Bayesian-calibrated global sensitivity analysis for mathematical models using generative AI. [PDF]

PLoS Comput Biol
Wang X.
europepmc   +1 more source

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

Advanced Intelligent Discovery, EarlyView.
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

A symbolic dataset for large language models to solve second kind Fredholm integral equations. [PDF]

Sci Data
Dana Mazraeh H, Eslami S, Afzal Aghaei A, Parand K.   +3 more
europepmc   +1 more source

Machine Learning Driven Inverse Design of Broadband Acoustic Superscattering

Advanced Intelligent Discovery, EarlyView.
Multilayer acoustic superscatterers are designed using machine learning to achieve broadband superscattering and strong sound insulation. By incorporating a weighted mean absolute error into the loss function, the forward and inverse neural networks accurately map structural parameters to spectral responses.
Lijuan Fan, Xiangliang Zhang, Ying Wu
wiley   +1 more source

Hybrid Vision Transformer-CNN Framework for Alzheimer's Disease Cell Type Classification: A Comparative Study with Vision-Language Models. [PDF]

J Imaging
Hasan ME, Fuad MTH, Sharif O, Wagler A.
europepmc   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
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

Enhanced swin transformer with dual attention for knee osteoarthritis severity grading from X-ray images. [PDF]

Sci Rep
Sudha K, Rajiv Kannan A.
europepmc   +1 more source

Revealing Protein–Protein Interactions Using a Graph Theory‐Augmented Deep Learning Approach

Advanced Intelligent Discovery, EarlyView.
This study presents a fast, cost‐efficient approach for classifying protein–protein interactions by integrating graph‐theory parametrization with deep learning (DL). Multiscale features extracted from graph‐encoded polarized‐light microscopy (PLM) images enable accurate prediction of binding strengths.
Bahar Dadfar, Gözde Kabay, Matthias Franzreb, Kody Whisnant, Nicholas A. Kotov, Joerg Lahann   +5 more
wiley   +1 more source

Quantum Computing for Transport Network Optimization. [PDF]

Entropy (Basel)
Ju J, Liu Z, Bai Y, Wang Y, Gao Q, Ma Y, Zheng C, Wen K.   +7 more
europepmc   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source