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
Programs:	This AO can be done under the following programs:   Program    Available To        SURF    Caltech students only  Click on a program name for program info and application requirements.