Scalable Empirical Dynamic Modeling With Parallel Computing and Approximate k-NN Search-Reference-Cited by-同舟云学术

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Scalable Empirical Dynamic Modeling With Parallel Computing and Approximate k-NN Search

Published:2023 Issue: Volume:11 Page:68171-68183
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Takahashi Keichi¹^ORCID,Ichikawa Kohei²^ORCID,Park Joseph³^ORCID,Pao Gerald M.⁴

Affiliation:

1. Cyberscience Center, Tohoku University, Sendai, Japan

2. Nara Institute of Science and Technology, Ikoma, Nara, Japan

3. Comprehensive Nuclear-Test-Ban Treaty Organization, Vienna, Austria

4. Okinawa Institute of Science and Technology, Onna, Okinawa, Japan

Funder

Japan Society for the Promotion of Science (JSPS) KAKENHI

Joint Usage/Research Center for Interdisciplinary Large-Scale Information Infrastructures (JHPCN), Japan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10164090.pdf?arnumber=10164090

Reference42 articles.

1. Nonlinear forecasting for the classification of natural time series;sugihara;Philos Trans Roy Soc Lond A Math Phys Eng Sci,1994

2. Fast near-whole–brain imaging in adult Drosophila during responses to stimuli and behavior

3. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series

4. ArrayFire: a GPU acceleration platform

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