Chinmay Datar, M.Sc.

Background

• Started my doctoral studies with Hans Fischer Senior Fellow Prof. Wil Schilders and Prof. Felix Dietrich in July 2022
• Doctoral candidate at the Institute of Advanced Study (IAS), TUM in the focus group: 'Scientific Machine Learning'
• MSc. in Computational Engineering at FAU, Erlangen (2018-2022)
  • Engineering Summer Internship at Koshizuka-Shibata Lab, University of Tokyo, Japan
  • Supported by the FAU Graduation Scholarship
• Summer Research Internship at the Hanson Lab, Stanford University, USA (2017)
• Bachelor's degree in Mechanical Engineering from the University of Pune (2017)

Teaching

Summer Semester 25:

• Seminar: High-Dimensional Methods for scientific computing (HDM)
• Lab course: Machine learning in crowd modeling and simulation

Winter semester 24/25:

• Scientific Computing and Machine Learning
• Seminar: Scientists and Ethics

Supervision: Student Theses

Ongoing Theses: 

• Yildiz, Eray (Master's Thesis): Solving PDEs with SWIM networks using domain decomposition.
• Semiz, Ahmet (Master's Thesis): Predicting fluid dynamics using convolutional random feature models.

Finished Theses:

• Atamert Rahma (Master's Thesis): Sampling neural networks to approximate Hamiltonian functions.
• Aditya Phopale (Master's Thesis): Using neural networks with domain decomposition to solve partial differential equations.

 

Research interests

I am working on developing methods that synergistically combine classical methods in scientific computing and deep learning for solving Partial Differential Equations (PDEs). In particular, I am currently working on the following ideas:  

• Understanding and developing novel neural network architectures for simulating dynamical systems.
• Back-propagation-free training of neural PDE solvers. 

• Combining domain decomposition and neural networks for solving PDEs. 

   

Publications

You can find my publications in the Google Scholar.