294 citations · 2.9k across the 83 of their papers we have counts for
14 papers · 1 filter
Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning
Eivind Meyer, Haakon Robinson, Adil Rasheed +1
In this article, we explore the feasibility of applying proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm for continuous control tasks, on the…
A long short-term memory embedding for hybrid uplifted reduced order models
Shady E. Ahmed, Omer San, Adil Rasheed +1
In this paper, we introduce an uplifted reduced order modeling (UROM) approach through the integration of standard projection based methods with long short-term memory (LSTM) embed…
An evolve-then-correct reduced order model for hidden fluid dynamics
Suraj Pawar, Shady E. Ahmed, O. San +1
In this paper, we put forth an evolve-then-correct reduced order modeling approach that combines intrusive and nonintrusive models to take hidden physical processes into account. S…
Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensors
Harsha Vaddireddy, Adil Rasheed, Anne E Staples +1
In this study we put forth a modular approach for distilling hidden flow physics in discrete and sparse observations. To address functional expressiblity, a key limitation of the b…
Data-driven recovery of hidden physics in reduced order modeling of fluid flows
Suraj Pawar, Shady E. Ahmed, Omer San +1
In this article, we introduce a modular hybrid analysis and modeling (HAM) approach to account for hidden physics in reduced order modeling (ROM) of parameterized systems relevant…
Sampling and resolution characteristics in reduced order models of shallow water equations: intrusive vs non-intrusive
Shady E. Ahmed, Omer San, Diana A. Bistrian +1
We investigate the sensitivity of reduced order models (ROMs) to training data resolution as well as sampling rate. In particular, we consider proper orthogonal decomposition (POD)…