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Grant Award View - GA142243
Estimating the Topology of Low-Dimensional Data Using Deep Neural...
GA ID:
GA142243
Agency:
Australian Research Council
Approval Date:
11-Dec-2020
Publish Date:
15-Dec-2020
Category:
Science, Technology, Engineering and Mathematics (STEM) Research
Grant Term:
15-Mar-2021 to 14-Mar-2024
Original: 1-Jan-2021 to 31-Dec-2023
Value (AUD):
$411,000.00
(GST inclusive where applicable)
Variations:
- GA142243-V3 - Variation to Grant (24-May-2022 )
- GA142243-V2 - Variation to Grant (20-Oct-2021 )
- GA142243-V1 - Variation to Grant (2-Aug-2021 )
One-off/Ad hoc:
No
Aggregate Grant Award:
No
PBS Program Name:
ARC 20/21 Discovery
Grant Program:
Discovery Projects
Grant Activity:
Estimating the Topology of Low-Dimensional Data Using Deep Neural Networks
Purpose:
This project will expand on the superhuman visual capabilities of deep neural networks to allow us to analyse the topology of 3- and 4-dimensional manifolds. While these spaces still count as low-dimensional, 4-dimensional manifolds typically are beyond human visual comprehension. The topology of a manifold describes its essential properties such as the number of connected components, holes, tunnels and cavities of various dimensions. Traditional methods from computational topology fail in large practical applications due to computational restrictions. We propose an approximation that overcomes previous limitations and can open new doors to data analysis in material science, medical imaging, dynamical systems and other applications.
GO ID:
GO Title:
Discovery Projects for funding commencing in 2021
Internal Reference ID:
DP21 Round 1
Selection Process:
Targeted or Restricted Competitive
Confidentiality - Contract:
No
Confidentiality - Outputs:
No
Grant Recipient Details
Recipient Name:
The University of Newcastle
Recipient ABN:
15 736 576 735
Grant Recipient Location
Suburb:
CALLAGHAN
Town/City:
CALLAGHAN
Postcode:
2308
State/Territory:
NSW
Country:
AUSTRALIA
Grant Delivery Location
State/Territory:
NSW
Postcode:
2308
Country:
AUSTRALIA