Chinmay Datar

PhD student, Technische Universität München

Joined

May 2023

Names

Chinmay Datar (Preferred)

Chinmay Vinod Datar

Emails

****@tum.de (Confirmed)

Personal Links

Homepage

Career & Education History

PhD student

Technische Universität München (tum.de)

2022 – Present

Advisors, Relations & Conflicts

PhD Advisor

Felix Dietrich

Present

PhD Advisor

Wil Schilders

Present

Expertise

scientific machine learning

Present

dynamical systems

Present

recurrent neural networks

Present

ordinary differential equations

Present

structure-preserving neural networks

Present

Publications

Fast Multiscale PDE Solvers via Multilevel Domain Decomposition and Random Features
Eray Yildiz, Chinmay Datar, Atamert Rahma, Victorita Dolean, Felix Dietrich
- AI&PDE Poster
- Readers: Everyone
Rapid Training of Hamiltonian Graph Networks Using Random Features
Atamert Rahma, Chinmay Datar, Ana Cukarska, Felix Dietrich
- ICLR 2026 Poster
- Readers: Everyone
Fast training of accurate physics-informed neural networks without gradient descent
Chinmay Datar, Taniya Kapoor, Abhishek Chandra, Qing Sun, Erik Lien Bolager, Iryna Burak, Anna Veselovska, Massimo Fornasier, Felix Dietrich
- ICLR 2026 Oral
- Readers: Everyone
Role of Parametrization in Learning Dynamics of Recurrent Neural Networks
Adwait Datar, Chinmay Datar, Zahra Monfared, Felix Dietrich
- NeurIPS 2024 Workshop
- Readers: Everyone
Backpropagation-free training of neural PDE solvers for time-dependent problems
Chinmay Datar, Taniya Kapoor, Abhishek Chandra, Qing Sun, Erik Lien Bolager, Iryna Burak, Anna Veselovska, Massimo Fornasier, Felix Dietrich
- Submitted to ICLR 2025
- Readers: Everyone
Continuous-time neural networks for modeling linear dynamical systems
Chinmay Datar, Adwait Datar, Felix Dietrich, Wil Schilders
- AI4DiffEqtnsInSci @ ICLR 2024 Poster
- Readers: Everyone

Chinmay Datar

Names

Emails

Personal Links

Career & Education History

Advisors, Relations & Conflicts

Expertise

Publications

Fast Multiscale PDE Solvers via Multilevel Domain Decomposition and Random Features

Rapid Training of Hamiltonian Graph Networks Using Random Features

Fast training of accurate physics-informed neural networks without gradient descent

Role of Parametrization in Learning Dynamics of Recurrent Neural Networks

Backpropagation-free training of neural PDE solvers for time-dependent problems

Continuous-time neural networks for modeling linear dynamical systems

Co-Authors