Results 181 to 190 of about 12,450 (263)

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
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

Predictive modeling for physicochemical properties of β-lactam antibiotics through eigenvalue based topological indices and non linear regression techniques. [PDF]

Sci Rep
Yuvaraj A, Kalaimurugan G, Thamizhmaran R, Tajudeen MM, Contreras RC.   +4 more
europepmc   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

Advanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu, Ksenia Zlobina, Michael Briden, Oscar Avalos, Héctor Carrión, Sebastian Osorio, Hsin‐ya Yang, Alexandra Nava, Michelle D. Bagood, Marco Rolandi, Roslyn Rivkah Isseroff, Marcella Gomez   +11 more
wiley   +1 more source

Toward Intelligent Multimodal Holography for Real‐Time Chemical Imaging of Dynamic Ion Separation

Advanced Intelligent Discovery, EarlyView.
Intelligent multimodal holography integrates digital off‐axis holography, spectroscopic imaging, and AI‐driven reconstruction to visualize ion transport and chemical dynamics in real time. In this perspective paper, we outline how this approach enables label‐free, chemically specific monitoring of complex environments and discuss its potential to ...
Giovanna Ricchiuti, Sarah I. Allec, Maxim Ziatdinov, Venkateshkumar Prabhakaran   +3 more
wiley   +1 more source

Switching metastable dynamics in many-body open quantum systems. [PDF]

Natl Sci Rev
Xiang YX, Li W, Bai Z, Ma YQ.
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

Galaxy Evolution with Manifold Learning. [PDF]

Entropy (Basel)
Takeuchi TT, Cooray S, Kano RR.
europepmc   +1 more source

Parametric Analysis of Spiking Neurons in 16 nm Fin Field‐Effect Transistor Technology

Advanced Intelligent Discovery, EarlyView.
Energy efficient computing has driven a shift toward brain‐inspired neuromorphic hardware. This study explores the design of three distinct silicon neuron topologies implemented in 16 nm fin field‐Effect transistor technology. While the Axon‐Hillock design achieves gigahertz throughput, its functional fragility persists. The Morris–Lecar model captures
Logan Larsh, Raiyan Siddique, Sarah S. Sharif, Yaser Mike Banad   +3 more
wiley   +1 more source

Intermittent Two-Point Dynamics at the Transition to Chaos for Random Circle Endomorphisms. [PDF]

Commun Math Phys
Goverse VPH, Homburg AJ, Lamb JSW.
europepmc   +1 more source

Spectral divide-and-conquer algorithms for matrix eigenvalue problems (Fusion of theory and practice in applied mathematics and computational science)

Spectral divide-and-conquer algorithms for matrix eigenvalue problems (Fusion of theory and practice in applied mathematics and computational science)
Conventional algorithms for the (symmetric or non-symmetric) eigenvalue decomposition and the singular value decomposition (SVD) are based on initially reducing the matrix to a condensed (tridiagonal, Hessenberg or bidiagonal) form. Unfortunately, they are not optimal in view of recent trends in computer architectures, which require minimizing ...
openaire