Results 151 to 160 of about 186,149 (308)
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
Monte carlo simulation of diffusions [PDF]
2008 Winter Simulation Conference, 2008 openaire +1 more source
Advanced Electronic Materials, EarlyView.ABSTRACT Machine learning and Artificial Intelligence (AI) tasks have stretched traditional hardware to its limits. In‐hardware computation is a novel approach that aims to run complex operations, such as matrix–vector multiplication, directly at the device level for increased efficiency.
Juan P. Martinez +10 more
wiley +1 more source
Monte Carlo simulations of dynamic radiotherapy treatments
, 2012 The effects of tumour motion during radiation therapy delivery have been widely investigated. Motion effects have become increasingly important with the introduction of dynamic radiotherapy delivery modalities such as enhanced dynamic wedges (EDWs) and ...
Kakakhel, Muhammad Basim
core
On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification
Advanced Electronic Materials, EarlyView.ABSTRACT Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor‐based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time‐series ...
Rishona Daniels +4 more
wiley +1 more source
A Monte Carlo approach to value exchange options using a single stochastic factor [PDF]
Exchange options give the holder the right to exchange one risky asset V for another risky asset D. The asset V is referred to as the optioned (underlying) asset, while D is the delivery asset.
Giovanni Villani
core
Advanced Energy Materials, EarlyView.In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu +6 more
wiley +1 more source
Parallel hierarchical sampling:a general-purpose class of multiple-chains MCMC algorithms [PDF]
This paper introduces the Parallel Hierarchical Sampler (PHS), a class of Markov chain Monte Carlo algorithms using several interacting chains having the same target distribution but different mixing properties.
Antonietta Mira, Fabio Rigat
core
Comparative Insights and Overlooked Factors of Interphase Chemistry in Alkali Metal‐Ion Batteries
Advanced Energy Materials, EarlyView.This review presents a comparative analysis of Li‐, Na‐, and K‐ion batteries, focusing on the critical role of electrode–electrolyte interphases. It especially highlights overlooked aspects such as SEI/CEI misconceptions, binder effects, and self‐discharge relevance, emphasizing the limitations of current understanding and offering strategies for ...
Changhee Lee +3 more
wiley +1 more source
Monte Carlo Simulation Approach to Calculate Value at Risk: Application to WIG20 and mWIG40
Nauki o Finansach, 2019 This paper reports our estimates of the Value at Risk using Monte Carlo simulations for which we developed a computer program. Our approach involves obtaining Monte Carlo parameters by fitting real historical data of different periods to probability ...
Aleksandra Helena Pasieczna
doaj