A data-driven approach to conditional screening of high-dimensional variables

High dimensional neural network potential (HDNNP) is interested as an alternative to classical force field calculations by data-driven approach. HDNNP has an advantage over classical force field calculation, such as being able to handle chemical reactions, but there are many points yet to be understood with respect to the chemical transferability in particular for non-organic compounds. In this paper, we focused on Au13+ and Au11+ clusters and showed that the energy of clusters of different sizes can be predicted by HDNNP with semi-quantitative accuracy.

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Health Monitoring of Large-Scale Civil Structures: An Approach Based on Data Partitioning and Classical Multidimensional Scaling

Sensors ◽

10.3390/s21051646 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1646

Author(s):

Alireza Entezami ◽

Hassan Sarmadi ◽

Behshid Behkamal ◽

Stefano Mariani

Keyword(s):

Multidimensional Scaling ◽

Damage Detection ◽

Health Monitoring ◽

Large Scale ◽

Environmental Variability ◽

Data Driven ◽

High Dimensional ◽

Data Driven Approach ◽

Low Dimensional ◽

High Dimensional Datasets

A major challenge in structural health monitoring (SHM) is the efficient handling of big data, namely of high-dimensional datasets, when damage detection under environmental variability is being assessed. To address this issue, a novel data-driven approach to early damage detection is proposed here. The approach is based on an efficient partitioning of the dataset, gathering the sensor recordings, and on classical multidimensional scaling (CMDS). The partitioning procedure aims at moving towards a low-dimensional feature space; the CMDS algorithm is instead exploited to set the coordinates in the mentioned low-dimensional space, and define damage indices through norms of the said coordinates. The proposed approach is shown to efficiently and robustly address the challenges linked to high-dimensional datasets and environmental variability. Results related to two large-scale test cases are reported: the ASCE structure, and the Z24 bridge. A high sensitivity to damage and a limited (if any) number of false alarms and false detections are reported, testifying the efficacy of the proposed data-driven approach.

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Bundling in Molecular Dynamics Simulations to Improve Generalization Performance in High-Dimensional Neural Network Potentials

10.26434/chemrxiv.7860032.v1 ◽

2019 ◽

Author(s):

Yasuharu Okamoto

Keyword(s):

Neural Network ◽

Force Field ◽

Data Driven ◽

High Dimensional ◽

Field Calculation ◽

Force Field Calculations ◽

Quantitative Accuracy ◽

Force Field Calculation ◽

Data Driven Approach ◽

Dynamics Simulations

High dimensional neural network potential (HDNNP) is interested as an alternative to classical force field calculations by data-driven approach. HDNNP has an advantage over classical force field calculation, such as being able to handle chemical reactions, but there are many points yet to be understood with respect to the chemical transferability in particular for non-organic compounds. In this paper, we focused on Au13+ and Au11+ clusters and showed that the energy of clusters of different sizes can be predicted by HDNNP with semi-quantitative accuracy.

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Detection and Identification of Cyber and Physical Attacks on Distribution Power Grids with PVs: An Online High-Dimensional Data-driven Approach

IEEE Journal of Emerging and Selected Topics in Power Electronics ◽

10.1109/jestpe.2019.2943449 ◽

2019 ◽

pp. 1-1 ◽

Cited By ~ 4

Author(s):

Fangyu Li ◽

Rui Xie ◽

Bowen Yang ◽

Lulu Guo ◽

Ping Ma ◽

...

Keyword(s):

High Dimensional Data ◽

Power Grids ◽

Data Driven ◽

High Dimensional ◽

Detection And Identification ◽

Data Driven Approach

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BLP-2LASSO for aggregate discrete choice models with rich covariates

Econometrics Journal ◽

10.1093/ectj/utz010 ◽

2019 ◽

Vol 22 (3) ◽

pp. 262-281 ◽

Cited By ~ 1

Author(s):

Benjamin J Gillen ◽

Sergio Montero ◽

Hyungsik Roger Moon ◽

Matthew Shum

Keyword(s):

Ad Hoc ◽

Demographic Data ◽

Discrete Choice Models ◽

Random Coefficients ◽

Data Driven ◽

Preference Heterogeneity ◽

High Dimensional ◽

Differentiated Products ◽

Estimation Algorithms ◽

Data Driven Approach

Summary We introduce the BLP-2LASSO model, which augments the classic BLP (Berry, Levinsohn, and Pakes, 1995) random-coefficients logit model to allow for data-driven selection among a high-dimensional set of control variables using the 'double-LASSO' procedure proposed by Belloni, Chernozhukov, and Hansen (2013). Economists often study consumers’ aggregate behaviour across markets choosing from a menu of differentiated products. In this analysis, local demographic characteristics can serve as controls for market-specific preference heterogeneity. Given rich demographic data, implementing these models requires specifying which variables to include in the analysis, an ad hoc process typically guided primarily by a researcher’s intuition. We propose a data-driven approach to estimate these models, applying penalized estimation algorithms from the recent literature in high-dimensional econometrics. Our application explores the effect of campaign spending on vote shares in data from Mexican elections.

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