Neural network surrogate models of gravitational waveforms

Project Title: Neural network surrogate models of gravitational waveforms
Program:
CAREERS (323)

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Tags:
AI/ML (802), astrophysics (297), deep-learning (303), parameter-sweeps (301)

Status: Complete
Project Leader
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Project Leader:
Scott Field

Email: sfield17@uri.edu
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Mentor(s):
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Student-facilitator(s):
Ashwin Girish (29730)

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Project Information
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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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Student Facilitator Programming Skill Level: Practical applications
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Project Institution: University of Rhode Island
Project Address:
Rhode Island

Anchor Institution: CR-University of Rhode Island
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Start as soon as possible.: No
Project Urgency: Already behind3Start date is flexible
Expected Project Duration (in months): 3
Launch Presentation: https://support.access-ci.org/system/files/webform/project/4538/LaunchPresentation_Ashwin.pptx
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Wrap Presentation: https://support.access-ci.org/system/files/webform/project/4538/Wrap_presentation_Ashwin.pptx
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Project Milestones:
- Milestone Title: Milestone#1 
  Milestone Description: Install and run existing Python codes on Unity; run interactive examples on CPU and GPU; launch presentation
  Completion Date Goal: 2024-06-30
- Milestone Title: Milestone#2
  Milestone Description: Write and submit job submission scripts for Unity for various target problems
  Completion Date Goal: 2024-07-31
- Milestone Title: Milestone #3
  Milestone Description: Carry out hyperparameter optimization studies for various target problems; wrap presentation; exit interview 
  Completion Date Goal: 2024-08-31

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Final Report
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