Results 101 to 110 of about 23,606 (263)

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

Advanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +1 more source

A New Switchable Zr12 Oxocluster Based Large Pore Metal–Organic Framework Functionalized With Spin Crossover Complexes for Acetic Acid Colorimetric Sensing

Angewandte Chemie, EarlyView.
We present here (1)⊂MIP‐216(Zr), a new hybrid material composed of an FeII spin crossover complex (1) loaded in the pores of a new hydrophobic metal–organic framework (MIP‐216(Zr), that presents a high affinity toward acetic acid and acts as a highly sensitive colorimetric sensor of acetic acid vapor due to a spin state switching of the encapsulated ...
Emmelyne Cuza, Chenchen Cao, Abeer Mohtar, Mohammad Wahiduzzaman, William Shepard, Guillaume Maurin, Moises L. Pinto, Christian Serre, Antoine Tissot   +8 more
wiley   +2 more sources

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

Generative and Experimental Validation of High Refractive Index Polymers via Domain Knowledge Approach with Small Data

Advanced Intelligent Discovery, EarlyView.
This research demonstrates that the combination of domain knowledge–based multiple regression, multi‐objective Bayesian optimization, and generative models is a suitable prediction tool for candidates of high refractive index polymers, even with the constraints in the model trained on limited data. The experimental validation can reproduce the proposed
Takuya Yokoo, Seigo Watanabe, Hana Matsunaga, Kenichi Oyaizu   +3 more
wiley   +1 more source

GREAPMC: development status, capabilities, and future developments

EPJ Nuclear Sciences & Technologies
This article focuses on the capabilities, performance, and verification of GPU-optimized REActor Physics Monte Carlo (GREAPMC), a multigroup Monte Carlo code, against multigroup simulation on MCS; an in-house, CPU-based, Monte Carlo code.
Ali Muhammad Rizwan, Aygul Murat Serdar, Lee Deokjung   +2 more
doaj   +1 more source

Parallelization of Monte Carlo Shielding Code MCACE and Monte Carlo Criticality Code KENO-IV.

Journal of the Atomic Energy Society of Japan / Atomic Energy Society of Japan, 1992
TAKANO, Makoto, MASUKAWA, Fumihiro, KOMURO, Yuichi, NAITO, Yoshitaka, KAWAZOE, Akemi, OKUDA, Motoi, FUJISAKI, Masahide, SUZUKI, Koichiro   +7 more
openaire   +1 more source

CGMF & FREYA Verification in Monte Carlo Code RMC

EPJ Web of Conferences
Monte Carlo simulation has become a crucial method internationally for simulating neutron multiplicity counting devices. This method requires sampling of fission neutron multiplicities, energies, and directions, making accurate simulation of fission events highly important. To meet this demand, the Fission Reaction Event Yield Algorithm (FREYA) and the
Gou Yuanhao, Liu Zhaoyuan, Jia Conglong, Wang Kan   +3 more
openaire   +2 more sources

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

Overcoming the Phase Discontinuity Problem in RIS Optimization: A Robust and Scalable Phase‐Aware Deep Regression Framework via Hybrid CNN–LSTM Architecture

Advanced Intelligent Systems, EarlyView.
The phase discontinuity problem—where the cyclic nature of phase angles causes catastrophic errors near the ±π boundary—is a fundamental obstacle in learning‐based reconfigurable intelligent surface (RIS) optimization. A phase‐aware hybrid CNN–LSTM framework resolves this by decomposing phase predictions into sine–cosine components, mapping circular ...
Seda Savaşçı Şen, Ali Çalhan, Murtaza Cicioğlu, Oğuzhan Demiryürek   +3 more
wiley   +1 more source

Status of Serpent Monte Carlo code in 2024

EPJ Nuclear Sciences & Technologies
The Serpent Monte Carlo code has been in public distribution for 15 years, and has a large international user basis with both research and commercial applications. Serpent is currently developed as part of the Kraken multi-physics framework, which has dedicated capabilities for core-level reactor physics analyses.
Jaakko Leppänen, Ville Valtavirta, Antti Rintala, Riku Tuominen   +3 more
openaire   +2 more sources