Moon image from the original Research page
Data assimilation and plasma physics

Researching the Flux-balanced Hasegawa–Wakatani model through filtering theory and computational fluid dynamics. We apply POD/SVD for dimensionality reduction to the model using Scikit-learn PCA and solve the governing equations through numerical methods. The resulting framework describes the turbulence that occurs in highly magnetized plasma in fusion-like environments.

Plasma turbulence visualization from the original site

Reference: Andrew J. Majda, Di Qi, and Antoine J. Cerfon. “A flux-balanced fluid model for collisional plasma edge turbulence: Model derivation and basic physical features.” Physics of Plasmas 25(10), 102307 (2018).

Data assimilation and control theory

Integrating Sparse Identification of Nonlinear Dynamics (SINDy) and the Ensemble Kalman Filter with adaptive inflation to predict trajectories of true signals from stochastic datasets. The resulting architecture is applied to modern chaotic dynamical systems such as the Lorenz-96 model, with the aim of improving current methods used to control a system through this approach.

Chaotic dynamical system visualization from the original site

Reference: Brian M. de Silva et al. “PySINDy: a Python package for the sparse identification of nonlinear dynamics from data.” arXiv (2020). arXiv:2004.08424