Mulia Iyan MULIA IYAN

Researcher Number	20842397
Other IDs	https://orcid.org/0000-0002-6962-7026
Affiliation (based on the past Project Information) *help	2022 – 2024: 国立研究開発法人理化学研究所, 開拓研究本部, 研究員
Review Section/Research Field	Principal Investigator Basic Section 25030:Disaster prevention engineering-related
Keywords	Principal Investigator Natural hazard / Real-time forecasting / Early warning / Disaster mitigation / Forecasting / Deep learning / Machine learning / Storm surge / Tsunami / Prediction

Research Projects
(1 results)
Research Products
(7 results)

Leveraging world's largest offshore observation networks and physics-based model integration with machine learning for real-time tsunami and storm surge forecasting along the Pacific coasts of JapanPrincipal Investigator
- Principal Investigator
  
  Mulia Iyan
- Project Period (FY)
  2022 – 2024
- Research Category
  
  Grant-in-Aid for Early-Career Scientists
- Review Section
  
  Basic Section 25030:Disaster prevention engineering-related
- Research Institution
  Institute of Physical and Chemical Research

All 2024 2023 2022

All Journal Article Presentation

[Journal Article] A novel deep learning approach for typhoon-induced storm surge modeling through efficient emulation of wind and pressure fields2023
- Author(s)
  Mulia Iyan E.、Ueda Naonori、Miyoshi Takemasa、Iwamoto Takumu、Heidarzadeh Mohammad
- Journal Title
  
  Scientific Reports
  
  Volume: 13 Issue: 1
- DOI
  10.1038/s41598-023-35093-9
- Open Access
- Data Source
  KAKENHI-PROJECT-22K14459
[Journal Article] Sensitivity of Tsunami Data to the Up-Dip Extent of the July 2021 Mw 8.2 Alaska Earthquake2022
- Author(s)
  Mulia Iyan E.、Gusman Aditya Riadi、Heidarzadeh Mohammad、Satake Kenji
- Journal Title
  
  Seismological Research Letters
  
  Volume: 93 Issue: 4 Pages: 1992-2003
- DOI
  10.1785/0220210359
- Peer Reviewed / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-22K14459
[Journal Article] Machine learning-based tsunami inundation prediction derived from offshore observations2022
- Author(s)
  Mulia Iyan E.、Ueda Naonori、Miyoshi Takemasa、Gusman Aditya Riadi、Satake Kenji
- Journal Title
  
  Nature Communications
  
  Volume: 13 Issue: 1
- DOI
  10.1038/s41467-022-33253-5
- Peer Reviewed / Open Access / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-22K14459
[Presentation] Explainable temporal fusion transformer for forecasting tropical cyclone intensity2024
- Author(s)
  Mulia, I. E., Shimada, U., Ueda, N., Miyoshi, T.
- Organizer
  AGU
- Data Source
  KAKENHI-PROJECT-22K14459
[Presentation] Deep learning application for emulating atmospheric forcings in storm surge modeling2023
- Author(s)
  Iyan E. Mulia
- Organizer
  Asia Oceania Geosciences Society (AOGS)
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-22K14459
[Presentation] The use of S-net data for tsunami inundation forecasting using machine learning2022
- Author(s)
  Mulia Iyan E.、Ueda Naonori、Miyoshi Takemasa、Gusman Aditya Riadi、Satake Kenji
- Organizer
  Japan Geoscience Union
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-22K14459
[Presentation] Alaska-Aleutians megathrust as demonstrated by the July 2021 Mw 8.2 event2022
- Author(s)
  Mulia Iyan E.、Heidarzadeh Mohammad、Gusman Aditya Riadi、Satake Kenji
- Organizer
  Asia Oceania Geosciences Society
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-22K14459

Mulia Iyan MULIA IYAN

Research Projects

Research Products

Leveraging world's largest offshore observation networks and physics-based model integration with machine learning for real-time tsunami and storm surge forecasting along the Pacific coasts of JapanPrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

[Journal Article] A novel deep learning approach for typhoon-induced storm surge modeling through efficient emulation of wind and pressure fields2023

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Sensitivity of Tsunami Data to the Up-Dip Extent of the July 2021 Mw 8.2 Alaska Earthquake2022

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Machine learning-based tsunami inundation prediction derived from offshore observations2022

Author(s)

Journal Title

DOI

Data Source

[Presentation] Explainable temporal fusion transformer for forecasting tropical cyclone intensity2024

Author(s)

Organizer

Data Source

[Presentation] Deep learning application for emulating atmospheric forcings in storm surge modeling2023

Author(s)

Organizer

Data Source

[Presentation] The use of S-net data for tsunami inundation forecasting using machine learning2022

Author(s)

Organizer

Data Source

[Presentation] Alaska-Aleutians megathrust as demonstrated by the July 2021 Mw 8.2 event2022

Author(s)

Organizer

Data Source