2335626
Project Grant
Overview
Grant Description
SBIR Phase I: Generative physics-informed AI for computational physics and model-based engineering development.
The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be the democratization and enhancement of physics-based simulation models in product engineering.
By developing a generative Bayesian physics-informed classifier (B-PIC) network, this project aims to make advanced simulation tools more accessible, reducing the need for specialized analysts.
This innovation has the potential to significantly lower development costs and time, enabling earlier and more frequent simulations in the product design process.
The resulting sustainable engineering practices will lead to longer-lasting, higher-performing products, benefiting various industries and contributing to economic growth.
Additionally, this technology will foster broader scientific and technological understanding by integrating recent advances in generative artificial intelligence into physical sciences, paralleling the impact seen in computer vision and natural language processing.
This Small Business Innovation Research (SBIR) Phase I project proposes to address the challenges of mastering and setting up analyst-caliber physics simulations.
The current process is complex, time-consuming, and requires extensive training.
By incorporating strategies from physics-informed Gaussian process (PIGP) and Bayesian physics-informed neural network (BPINN) architectures, the B-PIC network will integrate physics into its architecture, loss, and error functions.
This approach aims to minimize the need for package-specific expertise and promote efficient, accurate simulations.
The research objectives include developing the B-PIC network, optimizing the setup process for partial differential equations (PDEs), and demonstrating the system's effectiveness in reducing simulation time and cost.
The anticipated technical results will showcase the network's ability to transform physics simulation from a validation tool to a crucial development driver in product engineering.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.
Subawards are not planned for this award.
The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be the democratization and enhancement of physics-based simulation models in product engineering.
By developing a generative Bayesian physics-informed classifier (B-PIC) network, this project aims to make advanced simulation tools more accessible, reducing the need for specialized analysts.
This innovation has the potential to significantly lower development costs and time, enabling earlier and more frequent simulations in the product design process.
The resulting sustainable engineering practices will lead to longer-lasting, higher-performing products, benefiting various industries and contributing to economic growth.
Additionally, this technology will foster broader scientific and technological understanding by integrating recent advances in generative artificial intelligence into physical sciences, paralleling the impact seen in computer vision and natural language processing.
This Small Business Innovation Research (SBIR) Phase I project proposes to address the challenges of mastering and setting up analyst-caliber physics simulations.
The current process is complex, time-consuming, and requires extensive training.
By incorporating strategies from physics-informed Gaussian process (PIGP) and Bayesian physics-informed neural network (BPINN) architectures, the B-PIC network will integrate physics into its architecture, loss, and error functions.
This approach aims to minimize the need for package-specific expertise and promote efficient, accurate simulations.
The research objectives include developing the B-PIC network, optimizing the setup process for partial differential equations (PDEs), and demonstrating the system's effectiveness in reducing simulation time and cost.
The anticipated technical results will showcase the network's ability to transform physics simulation from a validation tool to a crucial development driver in product engineering.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.
Subawards are not planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23515
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Quincy,
Massachusetts
02169-5009
United States
Geographic Scope
Single Zip Code
Related Opportunity
Analysis Notes
Amendment Since initial award the total obligations have increased 7% from $274,970 to $294,970.
Parallel Pipes was awarded
Project Grant 2335626
worth $294,970
from National Science Foundation in September 2024 with work to be completed primarily in Quincy Massachusetts United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
SBIR Phase I
Title
SBIR Phase I: Generative Physics-Informed AI for Computational Physics and Model-Based Engineering Development
Abstract
The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be the democratization and enhancement of physics-based simulation models in product engineering. By developing a Generative Bayesian Physics-Informed Classifier (B-PIC) network, this project aims to make advanced simulation tools more accessible, reducing the need for specialized analysts. This innovation has the potential to significantly lower development costs and time, enabling earlier and more frequent simulations in the product design process. The resulting sustainable engineering practices will lead to longer-lasting, higher-performing products, benefiting various industries and contributing to economic growth. Additionally, this technology will foster broader scientific and technological understanding by integrating recent advances in generative artificial intelligence into physical sciences, paralleling the impact seen in computer vision and natural language processing.
This Small Business Innovation Research (SBIR) Phase I project proposes to address the challenges of mastering and setting up analyst-caliber physics simulations. The current process is complex, time-consuming, and requires extensive training. By incorporating strategies from Physics-Informed Gaussian Process (PIGP) and Bayesian Physics-Informed Neural Network (BPINN) architectures, the B-PIC network will integrate physics into its architecture, loss, and error functions. This approach aims to minimize the need for package-specific expertise and promote efficient, accurate simulations. The research objectives include developing the B-PIC network, optimizing the setup process for partial differential equations (PDEs), and demonstrating the system's effectiveness in reducing simulation time and cost. The anticipated technical results will showcase the network's ability to transform physics simulation from a validation tool to a crucial development driver in product engineering.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
AI
Solicitation Number
NSF 23-515
Status
(Ongoing)
Last Modified 8/12/25
Period of Performance
9/1/24
Start Date
8/31/25
End Date
Funding Split
$295.0K
Federal Obligation
$0.0
Non-Federal Obligation
$295.0K
Total Obligated
Activity Timeline
Transaction History
Modifications to 2335626
Additional Detail
Award ID FAIN
2335626
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
NC95ZKV4UHR3
Awardee CAGE
9KRC5
Performance District
MA-08
Senators
Edward Markey
Elizabeth Warren
Elizabeth Warren
Modified: 8/12/25