Harsha Vaddireddy

TL;DR: In this paper, a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows is proposed. But it is not suitable for modeling complex fluid flows.

TL;DR: A modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows is introduced and can be used as a robust predictive tool for non-intrusive model order reduction of complex fluid flows.

TL;DR: In this paper, a modular approach for distilling hidden flow physics from discrete and sparse observations is proposed, which combines evolutionary computation with feature engineering to provide a tool for discovering hidden parameterizations embedded in the trajectory of fluid flows in the Eulerian frame of reference.

TL;DR: In this paper, a modular approach for distilling hidden flow physics in discrete and sparse observations is proposed, which combines evolutionary computation with feature engineering to provide a tool to discover hidden parameterizations embedded in the trajectory of fluid flows in the Eulerian frame of reference.

TL;DR: This paper investigates the application of fast function extraction (FFX), a fast, scalable, deterministic symbolic regression algorithm to recover partial differential equations (PDEs), which identifies active bases among a huge set of candidate basis functions and their corresponding coefficients from recorded snapshot data.