Results 211 to 220 of about 1,208,874 (255)

Applying Monte Carlo Method for Straight-Line Model Sensor Calibration. [PDF]

Sensors (Basel)
Ramos PM, Janeiro FM.
europepmc   +1 more source

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

Gold Nanoparticle-Mediated DNA Damage Under FLASH Electron-Beam Irradiation: A Monte Carlo Study. [PDF]

Nanomaterials (Basel)
Kim CD, Chow JCL.
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

Commissioning and performance evaluations of the electron Monte Carlo algorithm in a new commercial treatment planning system. [PDF]

J Appl Clin Med Phys
Wang F, Jiang X, Shi Z, Liu Y, Zhang Y, Chen J.   +5 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

Influence of forehead gap size on eye protection provided by lead eyewear worn over prescription glasses: A Monte Carlo study. [PDF]

Med Phys
Shindo R, Fujisawa M, Yamamoto K, Sagehashi K, Inaba Y, Chida K.   +5 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

An analytical framework for efficient and precision-controlled Monte Carlo simulation of heterogeneous targeted alpha therapy microdosimetry. [PDF]

Med Phys
Zhu J, Cao S, Li X, Qi Y, Chen Z.
europepmc   +1 more source