An efficient and robust adaptive sampling method for polynomial chaos expansion in sparse Bayesian learning framework
Sign Up to like & getrecommendations! Published in 2019 at "Computer Methods in Applied Mechanics and Engineering"
DOI: 10.1016/j.cma.2019.04.046
Abstract: Abstract Sparse polynomial chaos expansion has been widely used to tackle problems of function approximation in the field of uncertain quantification. The accuracy of PCE depends on how to construct the experimental design. Therefore, adaptive… read more here.
Keywords: chaos expansion; polynomial chaos; sparse bayesian; adaptive sampling ... See more keywords
Adaptive sampling for active learning with genetic programming
Sign Up to like & getrecommendations! Published in 2021 at "Cognitive Systems Research"
DOI: 10.1016/j.cogsys.2020.08.008
Abstract: Abstract Active learning is a machine learning paradigm allowing to decide which inputs to use for training. It is introduced to Genetic Programming (GP) essentially thanks to the dynamic data sampling, used to address some… read more here.
Keywords: active learning; adaptive sampling; genetic programming; dynamic sampling ... See more keywords
Surrogate Modeling of Fugacity Coefficients Using Adaptive Sampling
Sign Up to like & getrecommendations! Published in 2019 at "Industrial & Engineering Chemistry Research"
DOI: 10.1021/acs.iecr.9b02758
Abstract: Complex thermodynamic models such as the perturbed chain statistical associating fluid theory (PC-SAFT) model describe the phase equilibria in a chemical process in a very precise way; however, because of their implicit and complex nature,… read more here.
Keywords: adaptive sampling; modeling fugacity; fugacity; fugacity coefficients ... See more keywords
LAST: Latent Space-Assisted Adaptive Sampling for Protein Trajectories
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.2c01213
Abstract: Molecular dynamics (MD) simulation is widely used to study protein conformations and dynamics. However, conventional simulation suffers from being trapped in some local energy minima that are hard to escape. Thus, most of the computational… read more here.
Keywords: adaptive sampling; space; latent space; seed ... See more keywords
Extensible and Scalable Adaptive Sampling on Supercomputers.
Sign Up to like & getrecommendations! Published in 2020 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.0c00991
Abstract: The accurate sampling of protein dynamics is an ongoing challenge despite the utilization of high-performance computer (HPC) systems. Utilizing only "brute force" molecular dynamics (MD) simulations requires an unacceptably long time to solution. Adaptive sampling… read more here.
Keywords: adaptive sampling; scalable adaptive; sampling; hpc ... See more keywords
Second-Order Orbital Optimization with Large Active Spaces Using Adaptive Sampling Configuration Interaction (ASCI) and Its Application to Molecular Geometry Optimization.
Sign Up to like & getrecommendations! Published in 2021 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.0c01292
Abstract: Recently, selected configuration interaction (SCI) methods that enable calculations with several tens of active orbitals have been developed. With the SCI subspace embedded in the mean field, molecular orbitals with an accuracy comparable to that… read more here.
Keywords: adaptive sampling; molecular geometry; geometry optimization; geometry ... See more keywords
Multiagent Reinforcement Learning-Based Adaptive Sampling for Conformational Dynamics of Proteins.
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.2c00683
Abstract: Machine learning is increasingly applied to improve the efficiency and accuracy of molecular dynamics (MD) simulations. Although the growth of distributed computer clusters has allowed researchers to obtain higher amounts of data, unbiased MD simulations… read more here.
Keywords: adaptive sampling; reinforcement learning; based adaptive; learning based ... See more keywords
Intelligent adaptive sampling guided by Gaussian process inference
Sign Up to like & getrecommendations! Published in 2017 at "Measurement Science and Technology"
DOI: 10.1088/1361-6501/aa7d31
Abstract: With the aim of reducing sampling density while having minimal impact on surface reconstruction accuracy, an adaptive sampling method based on Gaussian process inference is proposed. In each iterative step, the current sampling points serve… read more here.
Keywords: topography; adaptive sampling; process inference; gaussian process ... See more keywords
Adaptive sampling in higher dimensions for point-wise experimental measurement techniques
Sign Up to like & getrecommendations! Published in 2018 at "Measurement Science and Technology"
DOI: 10.1088/1361-6501/aac9da
Abstract: When performing point-wise measurements within a pre-defined domain, the experimentalist is faced with the problem of defining the spatial locations where to collect data based on an a priori unknown underlying signal. While structured sampling… read more here.
Keywords: adaptive sampling; sampling higher; point wise; measurement ... See more keywords
Dynamic, adaptive sampling during nanopore sequencing using Bayesian experimental design
Sign Up to like & getrecommendations! Published in 2022 at "Nature Biotechnology"
DOI: 10.1101/2020.02.07.938670
Abstract: Nanopore selective sequencing with real-time decision updates mitigates coverage bias. Nanopore sequencers can select which DNA molecules to sequence, rejecting a molecule after analysis of a small initial part. Currently, selection is based on predetermined… read more here.
Keywords: sequencing using; adaptive sampling; coverage; dynamic adaptive ... See more keywords
Path Tracing Denoising Based on SURE Adaptive Sampling and Neural Network
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.2999891
Abstract: A novel reconstruction algorithm is presented to address the noise artifacts of path tracing. SURE (Stein’s unbiased risk estimator) is adopted to estimate the noise level per pixel that guides adaptive sampling process. Modified MLPs… read more here.
Keywords: reconstruction; adaptive sampling; noise level; path tracing ... See more keywords