Title:
TRAJECTORY DATA PREDICTION DEVICE, TRAJECTORY DATA PREDICTION METHOD, AND PROGRAM
Document Type and Number:
WIPO Patent Application WO/2024/084622
Kind Code:
A1
Abstract:
A trajectory data prediction device according to one embodiment of the present disclosure has: a parameter learning unit that is configured to learn learnable parameters including parameters of Gaussian processes to which a vector field conforms, the vector field expressing a physical system in which conservation of energy or dissipation of energy is established, so as to maximize a probability distribution to which trajectory data conforms, the trajectory data representing the trajectory of a physical quantity observed in the physical system, and the learning being carried out on the basis of the similarity of the Gaussian processes; and a predicted trajectory data calculation unit that is configured to predict trajectory data satisfying the prediction condition by using the vector field, the prediction being made on the basis of the learnable parameters that have been learned and associated prediction conditions.
Inventors:
TANAKA YUSUKE (JP)
IWATA TOMOHARU (JP)
UEDA NAONORI (JP)
IWATA TOMOHARU (JP)
UEDA NAONORI (JP)
Application Number:
PCT/JP2022/038915
Publication Date:
April 25, 2024
Filing Date:
October 19, 2022
Export Citation:
Assignee:
NIPPON TELEGRAPH & TELEPHONE (JP)
International Classes:
G06N20/00
Domestic Patent References:
| WO2019235370A1 | 2019-12-12 |
Other References:
KATHARINA RATH: "Symplectic Gaussian process regression of maps in Hamiltonian systems", CHAOS., AMERICAN INSTITUTE OF PHYSICS, WOODBURY, NY., US, vol. 31, no. 5, 1 May 2021 (2021-05-01), US , XP093161037, ISSN: 1054-1500, DOI: 10.1063/5.0048129
YUSUKE TANAKA: "Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data", 36TH CONFERENCE ON NEURAL INFORMATION PROCESSING SYSTEMS (NEURIPS 2022), 31 October 2022 (2022-10-31), XP093161042, Retrieved from the Internet
YUSUKE TANAKA: "Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data", 36TH CONFERENCE ON NEURAL INFORMATION PROCESSING SYSTEMS (NEURIPS 2022), 31 October 2022 (2022-10-31), XP093161042, Retrieved from the Internet
Attorney, Agent or Firm:
ITOH, Tadashige et al. (JP)
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