Nisha Chandramoorthy

Update: I have moved to Georgia Tech as an assistant professor in the School of Computational Science and Engineering. I will no longer be updating this page, starting in January 2023.

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

  1. Chandramoorthy, N., Loukas, A., Gatmiry, K. and Jegelka, S. (2022) On the generalization of learning algorithms that do not converge (Accepted in NeurIPS 2022)
  2. 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)
  3. 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
  4. Chandramoorthy, N. and Wang, Q. (2021) An ergodic averaging method to differentiate covariant Lyapunov vectors. Code. Nonlinear Dynamics, 104, 4083–4102. Arxiv
  5. 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.
  6. 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
  7. Chandramoorthy, N., Magri, L. and Wang, Q. (2020) Variational optimization and data assimilation in chaotic time-delayed systems with automatic-differentiated shadowing sensitivity. Code
  8. Sliwiak, A., Chandramoorthy, N. and Wang, Q. (2020). Ergodic sensitivity analysis of one-dimensional chaotic maps. Theoretical & Applied Mechanics Letters. Arxiv
  9. Chandramoorthy, N. and Wang, Q. (2020) A computable realization of Ruelle's formula for linear response of statistics in chaotic systems.  Code and data
  10. 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.
  11. Chandramoorthy, N. and Hadjiconstantinou, N. G. (2018). Solving lubrication problems at the nanometer scale. Microfluidics and Nanofluidics, 22(4), 48. Arxiv. Code
Conference proceedings
  1. Chandramoorthy, N., Wang, Q. (2019). Sensitivity computation of statistically stationary quantities in turbulent flows. AIAA Aviation 2019-3426. (Best student paper). Arxiv
  2. 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.
  3. 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
  1. Chandramoorthy, N. An efficient algorithm for sensitivity analysis of chaotic systems. PhD Thesis. Massachusetts Institute of Technology, 2021. Advisor: Prof. Qiqi Wang
  2. Chandramoorthy, N. Molecular dynamics-based approaches for mesoscale lubrication. Master's Thesis. Massachusetts Institute of Technology, 2016. Advisor: Prof. Nicolas Hadjiconstantinou
  3. 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
  1. SIAM News article titled ``What Can We Learn from Sensitivity Analysis of One-dimensional Chaos?'' (with Adam Sliwiak and Qiqi Wang).