Announcements of Opportunity
SURF: Announcements of Opportunity
Below are Announcements of Opportunity posted by Caltech faculty and JPL technical staff for the SURF program. Each AO indicates whether or not it is open to non-Caltech students. If an AO is NOT open to non-Caltech students, please DO NOT contact the mentor. Announcements of Opportunity are posted as they are received. Please check back regularly for new AO submissions!
Remember: This is just one way that you can go about identifying a suitable project and/or mentor. Click here for more tips on finding a mentor. Announcements for external summer programs are listed here.
*Students applying for JPL projects should complete a SURF@JPL application instead of a "regular" SURF application.
*Students pursuing opportunities at JPL must be U.S. citizens or U.S. permanent residents.
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| Project: | Numerical simulations and data generation for operator learning | ||||||||
| Disciplines: | Engineering and Applied Science, Applied and Computational Mathematics | ||||||||
| Mentor: | Kaushik Bhattacharya, Howell Tyson Professor and Vice Provost (Research), (EAS), bhatta@caltech.edu | ||||||||
| Mentor URL: | https://mechmat.caltech.edu/ (opens in new window) | ||||||||
| AO Contact: | Harkirat Singh, hharkira@caltech.edu | ||||||||
| Background: | We are looking for a motivated student to assist in preparing a comprehensive data catalog through numerical simulations of a general partial differential equation (PDE) subjected to various initial and boundary conditions. This generalized framework encompasses a wide range of cases, including elasticity, viscoelasticity, phase transitions, and nonlocality. The generated data will serve as a foundation for advancing systematic understanding of the operator learning problem in machine learning research. | ||||||||
| Description: | This project provides an exciting opportunity to work at the intersection of computational science and state-of-the-art machine learning research. Familiarity with linear algebra and ordinary and partial differential equations (e.g., ACM 104 and ACM 95 or equivalent) is required, along with proficiency in Python and MATLAB. These skills are essential for performing simulations and processing the resulting data effectively. | ||||||||
| Student Requirements: |
Python/Matlab. ACM 104 and ACM 95 or equivalent |
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| Programs: |
This AO can be done under the following programs:
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