• A deep learning enabler for non-intrusive reduced order modeling of fluid flows 

      Pawar, Suraj; Rahman, Sk. Mashfiqur; Vaddireddy, H; San, Omer; Rasheed, Adil; Vedula, Prakash (Journal article; Peer reviewed, 2019)
      In this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows. We propose various DNN architectures which numerically predict ...
    • Digital Twin: Values, Challenges and Enablers from a modelling perspective 

      Rasheed, Adil; San, Omer; Kvamsdal, Trond (Journal article; Peer reviewed, 2020)
      Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances ...
    • Memory embedded non-intrusive reduced order modeling of non-ergodic flows 

      Ahmed, Shady E; Rahman, Sk. Mashfiqur; San, Omer; Rasheed, Adil; Navon, Ionel M (Journal article; Peer reviewed, 2019)
      Generating a digital twin of any complex system requires modeling and computational approaches that are efficient, accurate, and modular. Traditional reduced order modeling techniques are targeted at only the first two, ...