Results 231 to 240 of about 1,160,391 (297)

Age-Specific Monte Carlo Simulation-Based Microbial Risk Assessment of Drinking Water Contamination in Islamabad: Integrating Hydrochemical Analysis. [PDF]

Risk Anal
Haider Z, Ali W, Mahmood S, Khalid A.
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Robust Optimization of GMAW Parameters in 6063-T5 Aluminium Welds Using Taguchi Design, ANOVA, and Monte Carlo Simulation. [PDF]

Materials (Basel)
Meseguer-Valdenebro JL, Martínez Conesa EJ, Vergara D.   +2 more
europepmc   +1 more source

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

Comparison of three irradiation ways with Monte Carlo calculation for the feline lymphoma with orthovoltage radiation therapy. [PDF]

J Vet Med Sci
Nemoto Y, Tanabe Y, Onizuka R, Nakaichi M.   +3 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

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

Monte Carlo-based characterization of proton minibeam radiation therapy across clinically relevant beam parameters. [PDF]

Phys Imaging Radiat Oncol
Corvino A, Schneider T, Prezado Y.
europepmc   +1 more source

A Breadth‐First Pruned‐Enriched Rosenbluth Method for Force–Extension Simulations of Confined Semiflexible Chains

Advanced Intelligent Discovery, EarlyView.
The behaviors of semiflexible polymers such as DNA and protein are often reshaped by coupled interactions. Monte Carlo simulations assist in studying these systems. This work recasts the traditional chain‐growth strategy into a new framework: a fixed number of chains grow synchronously, while less relevant chains to the target system are removed and ...
Yihan Zhao, Jizeng Wang
wiley   +1 more source

Generative Autoencoders Coupled to Monte Carlo Simulation Allow Efficient Protein Conformation Sampling. [PDF]

J Chem Theory Comput
Beránek J, Tedeschi G, Spiwok V.
europepmc   +1 more source