Results 81 to 90 of about 12,230 (257)

Control Functionals for Quasi-Monte Carlo Integration

, 2015
Quasi-Monte Carlo (QMC) methods are being adopted in statistical applications due to the increasingly challenging nature of numerical integrals that are now routinely encountered. For integrands with $d$-dimensions and derivatives of order $α$, an optimal QMC rule converges at a best-possible rate $O(N^{-α/d})$. However, in applications the value of $α$
Oates, Chris J, Girolami, Mark
openaire   +4 more sources

Deciphering Intricacies in Directional CO2 Conversion From Electrolysis to CO2 Batteries

Advanced Energy Materials, EarlyView.
This review will delve into the inherent connections and distinctions of CO2‐directed conversion in ECO2RR and CO2 batteries, in terms of product types, catalyst selection, catalytic mechanisms, and electrochemical performances, while proposing a benchmarking framework for the evaluation of CO2 batteries and innovative CO2 battery configurations for ...
Changfan Xu, Huaping Zhao, Lizhuang Chen, Weidong Shi, Guosheng Shao, Yong Lei   +5 more
wiley   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information. [PDF]

Med Decis Making, 2022
Fang W, Wang Z, Giles MB, Jackson CH, Welton NJ, Andrieu C, Thom H.   +6 more
europepmc   +1 more source

Quasi-Monte Carlo with One Categorical Variable

Journal of Computational and Graphical Statistics
We study randomized quasi-Monte Carlo (RQMC) estimation of a multivariate integral where one of the variables takes only a finite number of values. This problem arises when the variable of integration is drawn from a mixture distribution as is common in importance sampling and also arises in some recent work on transport maps.
Valerie N. P. Ho, Art B. Owen, Zexin Pan
openaire   +2 more sources

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

Fast uncertainty quantification of tracer distribution in the brain interstitial fluid with multilevel and quasi Monte Carlo. [PDF]

Int J Numer Method Biomed Eng, 2021
Croci M, Vinje V, Rognes ME.
europepmc   +1 more source

Quasi-Monte Carlo Flows

EasyChair Preprints, 2018
Normalizing flows provide a general approach to construct flexible variational posteriors. The parameters are learned by stochastic optimization of the variational bound, but inference can be slow due to high variance of the gradient estimator. We propose Quasi-Monte Carlo (QMC) flows which reduce the variance of the gradient estimator by one order of ...
Florian Wenzel, Alexander Buchholz, Stephan Mandt   +2 more
openaire   +2 more sources

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

Parallel Randomized Quasi-Monte Carlo Simulation for Asian Basket Option Pricing

Journal of Algorithms & Computational Technology, 2012
High-dimensional derivatives pricing, such as Asian basket options, poses great computational challenges in practice. In this paper, parallel Randomized Quasi-Monte Carlo (RQMC) simulation method is investigated to tackle this kind of intractable ...
Yong-Hong Hu, Da-Qian Chen
doaj   +1 more source