Submission information
Submission Number: 200
Submission ID: 4538
Submission UUID: 5e729a48-6023-452e-8532-1c9fbd0ed529
Submission URI: /form/project
Created: Wed, 05/15/2024 - 15:32
Completed: Wed, 05/15/2024 - 15:42
Changed: Tue, 04/22/2025 - 11:08
Remote IP address: 131.109.33.100
Submitted by: Gaurav Khanna
Language: English
Is draft: No
Webform: Project
| Project Title | Neural network surrogate models of gravitational waveforms |
|---|---|
| Program | CAREERS |
| Project Image | |
| Tags | AI/ML (802), astrophysics (297), deep-learning (303), parameter-sweeps (301) |
| Status | Complete |
| Project Leader | Scott Field |
| sfield17@uri.edu | |
| Mobile Phone | |
| Work Phone | |
| Mentor(s) | |
| Student-facilitator(s) | Ashwin Girish |
| Mentee(s) | |
| Project Description | The gravitational wave (GW) signal received by an interferometric detector is an oscillatory "chirp" signal with its amplitude and frequency peaking at merger. Such waveforms are challenging to model directly because they contain a lot of structure and additional modulations resulting from precession effects and higher-order modes. Over the past decade, data-driven surrogate models have become prominent in GW data analysis, resulting in efficient and accurate surrogate models for a given set of GW signals with the help of numerical methods for decomposing, compressing, and fitting the original data. We propose to extend existing surrogate methods with the overarching goal of accelerating evaluation times while retaining accuracy by replacing current regression techniques -- such as greedy polynomial fits -- with artificial neural networks (ANNs), which have been shown to be universal function approximators. Given sufficient depth, ANNs can be very powerful and efficiently approximate arbitrary functions. For regression, one usually minimizes the mean squared error of the target function. We propose to model data pieces for processing GW signals with ANNs, making use of hyper-parameter optimization to find optimal values for the number of layers and neurons of ANNs as well as an optimal choice of the activation function. The student will work with an existing Python codebase that relies on PyTorch / Tensorflow deep learning packages. The student will become familiar with working on UNITY, writing and submitting bash submission scripts, using GPU acceleration, training ANNs, inspecting loss curves, and working and contributing with an existing Python codebase and version with git. |
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| Project Deliverables | |
| Student Research Computing Facilitator Profile | |
| Mentee Research Computing Profile | |
| Student Facilitator Programming Skill Level | Practical applications |
| Mentee Programming Skill Level | |
| Project Institution | University of Rhode Island |
| Project Address | Rhode Island |
| Anchor Institution | CR-University of Rhode Island |
| Preferred Start Date | |
| Start as soon as possible. | No |
| Project Urgency | Already behind3Start date is flexible |
| Expected Project Duration (in months) | 3 |
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| Project Milestones |
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| What will the student learn? | |
| What will the mentee learn? | |
| What will the Cyberteam program learn from this project? | |
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| Notes | |
| What is the impact on the development of the principal discipline(s) of the project? | |
| What is the impact on other disciplines? | |
| Is there an impact physical resources that form infrastructure? | |
| Is there an impact on the development of human resources for research computing? | |
| Is there an impact on institutional resources that form infrastructure? | |
| Is there an impact on information resources that form infrastructure? | |
| Is there an impact on technology transfer? | |
| Is there an impact on society beyond science and technology? | |
| Lessons Learned | |
| Overall results |