- Postdoctoral Researcher, Institute for Data, Systems and Society
- Massachusetts Institute of Technology
- nishac at mit dot edu, MIT Room: E18-404
- Accessibility

From Fall 2021, I am a postdoc mentored by Youssef Marzouk and Stefanie Jegelka. I am working with them on improving filtering of chaotic dynamical systems and on studying the dynamics of learning algorithms. I am also interested in computational and mathematical problems arising in climate studies, in fundamental analyses of low-order models as well as in developing scalable computational algorithms for detailed models.

Before this, I was a PhD student at the Aerospace Computational Design Lab at MIT. I worked with my PhD advisor, Qiqi Wang, on developing a new method for efficiently computing the derivatives with respect to system parameters of statistical or long-time averages in certain (idealized) classes of chaotic dynamical systems.

Papers and Preprints

- Chandramoorthy, N., Loukas, A., Gatmiry, K. and Jegelka, S. (2022) On the generalization of learning algorithms that do not converge
*(Accepted in NeurIPS 2022)* - Chandramoorthy, N. and Jezequel, M. (2022) Rigorous justification for the space-split sensitivity algorithm to compute linear response in Anosov systems, Nonlinearity, 35:8 Arxiv.
*(Authors in alphabetical order)* - Chandramoorthy, N. and Wang, Q. (2022) Efficient computation of linear response of chaotic attractors with one-dimensional unstable manifolds. SIAM Journal on Applied Dynamical Systems, 21:2. Arxiv. Code
- Chandramoorthy, N. and Wang, Q. (2021) An ergodic averaging method to differentiate covariant Lyapunov vectors. Code. Nonlinear Dynamics, 104, 4083–4102. Arxiv
- Chandramoorthy, N. and Wang, Q. (2021) On the probability of finding a nonphysical solution through shadowing. Journal of Computational Physics, 440, 110389 Arxiv Supplementary Material.
- Śliwiak, A. and Chandramoorthy, N. and Wang, Q. (2021) Computational assessment of smooth and rough parameter dependence of statistics in chaotic dynamical systems. Communications in Nonlinear Science and Numerical Simulation, 101, 105906.Arxiv
- Chandramoorthy, N., Magri, L. and Wang, Q. (2020) Variational optimization and data assimilation in chaotic time-delayed systems with automatic-differentiated shadowing sensitivity. Code
- Sliwiak, A., Chandramoorthy, N. and Wang, Q. (2020). Ergodic sensitivity analysis of one-dimensional chaotic maps. Theoretical & Applied Mechanics Letters. Arxiv
- Chandramoorthy, N. and Wang, Q. (2020) A computable realization of Ruelle's formula for linear response of statistics in chaotic systems. Code and data
- Chandramoorthy, N., Fernandez, P., Talnikar, C., and Wang, Q. (2019). Feasibility analysis of ensemble sensitivity computation in turbulent flows. AIAA Journal, 57(10), 4514-4526. Arxiv. Code.
- Chandramoorthy, N. and Hadjiconstantinou, N. G. (2018). Solving lubrication problems at the nanometer scale. Microfluidics and Nanofluidics, 22(4), 48. Arxiv. Code

Conference proceedings

- Chandramoorthy, N., Wang, Q. (2019). Sensitivity computation of statistically stationary quantities in turbulent flows. AIAA Aviation 2019-3426. (Best student paper). Arxiv
- Chandramoorthy, N., Fernandez, P., Talnikar, C. and Wang, Q. (2017). An analysis of the ensemble adjoint approach to sensitivity analysis in chaotic systems. 23rd AIAA Computational Fluid Dynamics Conference.
- Chandramoorthy, N., Wang, Z.N., Wang, Q. and Tucker, P. (2018). Toward computing sensitivities of average quantities in turbulent flows. Proceedings of the Center for Turbulence Research Summer Program

Theses

- Chandramoorthy, N. An efficient algorithm for sensitivity analysis of chaotic systems. PhD Thesis. Massachusetts Institute of Technology, 2021. Advisor: Prof. Qiqi Wang
- Chandramoorthy, N. Molecular dynamics-based approaches for mesoscale lubrication. Master's Thesis. Massachusetts Institute of Technology, 2016. Advisor: Prof. Nicolas Hadjiconstantinou
- Chandramoorthy, N. The fast multipole method in particle vortex methods. Bachelor's Thesis. Indian Institute of Technology Roorkee, 2014. Advisors: Prof. Praveen Chandrashekar, Prof. Karthik Duraisamy and Prof. Bhanu K. Mishra

Other articles, talks and posters

- SIAM News article titled ``What Can We Learn from Sensitivity Analysis of One-dimensional Chaos?'' (with Adam Sliwiak and Qiqi Wang).