NA24OARX021G0008
Project Grant
Overview
Grant Description
Purpose: We aim to develop software facilitating accurate and efficient simulations of extreme events, providing analysts with the tools they need to make better-informed decisions faster.
In a world shaped by climate change, high-fidelity physical simulations have the potential to revolutionize disaster preparedness and infrastructure resilience.
However, the computational expense and complexity of handling physical intricacies have posed significant barriers to the broader adoption of simulation.
To overcome these challenges, we will integrate machine learning for data-driven material modeling with modern meshfree computational methods.
Our initial focus will be on landslide simulations.
In Phase I, our goal is to create proof-of-concept, modern, meshfree software by implementing thermodynamically consistent recurrent neural network (TCRNN) models.
This will address the challenges of modeling complex materials, marking a pivotal advancement for the next generation of extreme event simulation tools.
By effectively tackling key challenges in extreme event simulation, our software facilitates the application of cutting-edge technology in government agencies and industries, expediting its use in addressing climate-related challenges.
In a world shaped by climate change, high-fidelity physical simulations have the potential to revolutionize disaster preparedness and infrastructure resilience.
However, the computational expense and complexity of handling physical intricacies have posed significant barriers to the broader adoption of simulation.
To overcome these challenges, we will integrate machine learning for data-driven material modeling with modern meshfree computational methods.
Our initial focus will be on landslide simulations.
In Phase I, our goal is to create proof-of-concept, modern, meshfree software by implementing thermodynamically consistent recurrent neural network (TCRNN) models.
This will address the challenges of modeling complex materials, marking a pivotal advancement for the next generation of extreme event simulation tools.
By effectively tackling key challenges in extreme event simulation, our software facilitates the application of cutting-edge technology in government agencies and industries, expediting its use in addressing climate-related challenges.
Funding Goals
18 CLIMATE ADAPTATION AND MITIGATION 19 WEATHER-READY NATION 20 HEALTHY OCEANS 21 RESILIENT COASTAL COMMUNITIES AND ECONOMIES
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Peralta,
New Mexico
870428858
United States
Geographic Scope
Single Zip Code
Related Opportunity
Aperi Computational Mechanics Consulting was awarded
Project Grant NA24OARX021G0008
worth $174,835
from National Oceanic and Atmospheric Administration in August 2024 with work to be completed primarily in Peralta New Mexico United States.
The grant
has a duration of 5 months and
was awarded through assistance program 11.021 NOAA Small Business Innovation Research (SBIR) Program.
The Project Grant was awarded through grant opportunity NOAA SBIR FY 2024 Phase I.
SBIR Details
Research Type
SBIR Phase I
Title
Mitigating the Impact: Advancing Extreme Event Simulations with Machine Learning-Enhanced Meshfree Methods
Abstract
We aim to develop software facilitating accurate and efficient simulations of extreme events, providing analysts with the tools they need to make better-informed decisions faster. In a world shaped by climate change, high-fidelity physical simulations have the potential to revolutionize disaster preparedness and infrastructure resilience. However, the computational expense and complexity of handling physical intricacies have posed significant barriers to the broader adoption of simulation. To overcome these challenges, we will integrate machine learning for data-driven material modeling with modern meshfree computational methods. Our initial focus will be on landslide simulations. In Phase I, our goal is to create proof-of-concept, modern, meshfree software by implementing Thermodynamically Consistent Recurrent Neural Network (TCRNN) models. This will address the challenges of modeling complex materials, marking a pivotal advancement for the next generation of extreme event simulation tools. By effectively tackling key challenges in extreme event simulation, our software facilitates the application of cutting-edge technology in government agencies and industries, expediting its use in addressing climate-related challenges.
Topic Code
9.1
Solicitation Number
NOAA-OAR-TPO-2024-2008184
Status
(Complete)
Last Modified 11/19/24
Period of Performance
8/1/24
Start Date
1/31/25
End Date
Funding Split
$174.8K
Federal Obligation
$0.0
Non-Federal Obligation
$174.8K
Total Obligated
Activity Timeline
Transaction History
Modifications to NA24OARX021G0008
Additional Detail
Award ID FAIN
NA24OARX021G0008
SAI Number
NA24OARX021G0008-001
Award ID URI
None
Awardee Classifications
Small Business
Awarding Office
1305N2 DEPT OF COMMERCE NOAA
Funding Office
1333BR OFC OF PROG.PLANNING&INTEGRATION
Awardee UEI
SEHQTLE77PL6
Awardee CAGE
9H8R5
Performance District
NM-01
Senators
Martin Heinrich
Ben Luján
Ben Luján
Modified: 11/19/24