Results 111 to 120 of about 313,686 (220)

Application of Artificial Intelligence in Mathematical Modeling and Numerical Investigation of Transport Processes in Electromembrane Systems. [PDF]

Membranes (Basel)
Kazakovtseva E, Kirillova E, Kovalenko A, Urtenov M.   +3 more
europepmc   +1 more source

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

Advanced Electronic Materials, EarlyView.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

Introduction: frontiers of applied inverse problems in science and engineering. [PDF]

Philos Trans A Math Phys Eng Sci
Smyl D, Hauptmann A, Tallman T.
europepmc   +1 more source

SPICE‐Compatible Compact Modeling of Cuprate‐Based Memristors Across a Wide Temperature Range

Advanced Electronic Materials, EarlyView.
A physics‐guided compact model for YBCO memristors is introduced, incorporating carrier trapping, field‐induced detrapping, and a differential balance equation to describe their switching dynamics. The model is compared with experiments and implemented in LTspice, allowing realistic circuit‐level simulations.
Thomas Günkel, Aleix Barrera, Lluís Balcells, Narcís Mestres, Enrique Miranda, Anna Palau, Jordi Suñé   +6 more
wiley   +1 more source

A brain-inspired computational framework for image-based risk assessment. [PDF]

Sci Rep
Zhou F, Hu S, Du X, Li N, Zhou T.
europepmc   +1 more source

Inversion of the Impedance Response Towards Physical Parameter Extraction Using Interpretable Machine Learning

Advanced Energy Materials, EarlyView.
ABSTRACT Interpreting the impedance response of perovskite solar cells (PSCs) is challenging due to the complex coupling of ionic and electronic motion. While drift‐diffusion (DD) modelling is a reliable method, its mathematical complexity makes directly extracting physical parameters from experimental data infeasible.
Mahmoud Nabil, Isel Grau, Ricardo Grau‐Crespo, Said Hamad, Juan A. Anta   +4 more
wiley   +1 more source

Artificial neural network modeling and optimization of an electrochemical biosensor for plasma miR-155-based breast cancer detection. [PDF]

Sci Rep
Imani A, Hosseinpour S, Azimzadeh M, Salehi A.   +3 more
europepmc   +1 more source

Addressing Bottlenecks to Achieve High‐Energy Sodium‐Ion Cells Using Tin Anodes

Advanced Energy Materials, EarlyView.
The challenges in coupling high voltage cathode with Sn‐alloying anode or in anode‐free cell, using glyme based electrolytes to realize high energy Na‐ion cells are reported. Surface protection strategy such as SnO coating on Sn or trapping the parasitic species from poisoning the anode, helps improving the cyclability, with ∼100% capacity retention ...
Parth Desai, Harshita Lohani, Wang Yu, François Ribot, Guillaume P. Laurent, Julien Bonefacino, Hervé Vezin, Dominique Foix, Carine Davoisne, Biao Zhang, Jean‐Marie Tarascon, Sathiya Mariyappan   +11 more
wiley   +1 more source

Editorial: Deep neural network architectures and reservoir computing. [PDF]

Front Artif Intell
Nobukawa S, Bhattacharya AK, Hirose A.
europepmc   +1 more source

Monitoring and control of a continuous, integrated filtration‐drying system with in‐line mass spectrometry via PharmaPy

AIChE Journal, EarlyView.
Abstract This article demonstrates the integration of in‐line mass spectrometry as a process analytical technology (PAT) tool with model‐based soft sensors in a continuous filtration‐drying carousel system for solid–liquid separation (SLS) of crystal slurries.
Inyoung Hur, Daniel Casas‐Orozco, Gintaras Reklaitis, Zoltan K. Nagy   +3 more
wiley   +1 more source