Mehul Goyal

I'm an undergraduate student from India currently exploring the field of Scientific Machine Learning — where machine learning meets scientific computing. My interests lie in applying Python-based tools to problems like solving differential equations, accelerating fluid simulations, and enhancing noisy or low-resolution data, such as in medical imaging.

Currently, I'm working at the Center for Scientific Computing and Computational Mechanics at the Indian Institute of Technology, Delhi, where I’m contributing to operator learning–based models, which have shown great promise in scientific simulations.

Though early in my journey, I'm actively learning and contributing to projects that use open-source Python frameworks like DeepXDE, NVIDIA Modulus (now PhysicsNeMo), NeuralOperator, and JAX. My current focus is on Physics-Informed Machine Learning (PI-ML), a field I hope to contribute to through research and practical applications in the coming years.

I'm looking forward to learning— how innovations in Python tooling, DevOps, and model deployment might enhance workflows in scientific machine learning. This is my first conference talk, where I hope to share what I'm learning and connect with the Python community driving much of this progress.