• 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 ...
    • COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning 

      Meyer, Eivind; Heiberg, Amalie; Rasheed, Adil; San, Omer (Peer reviewed; Journal article, 2020)
      Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to academic study, leading ...
    • Data-driven recovery of hidden hysics in reduced order modeling of fluid flows 

      Pawar, Suraj; Ahmed, Shady E; San, Omer; Rasheed, Adil (Peer reviewed; Journal article, 2020)
      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 to fluid dynamics. The hybrid ROM framework ...
    • Data-driven recovery of hidden physics in reduced order modeling of fluid flows 

      Pawar, Suraj; Ahmed, Shady E; San, Omer; Rasheed, Adil (Peer reviewed; Journal article, 2020)
      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 to fluid dynamics. The hybrid ROM framework ...
    • Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles 

      Havenstrøm, Simen Theie; Rasheed, Adil; San, Omer (Peer reviewed; Journal article, 2020)
      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 dual-objective problem of controlling an ...
    • 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 ...
    • An evolve-then-correct reduced order model for hidden fluid dynamics 

      Pawar, Suraj; Ahmed, Shady E; San, Omer; Rasheed, Adil (Journal article; Peer reviewed, 2020)
      n 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. Specifically, we split the underlying dynamics ...
    • Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data 

      Vaddireddy, Harsha; Rasheed, Adil; Staples, Anne; San, Omer (Peer reviewed; Journal article, 2020)
      We put forth a modular approach for distilling hidden flow physics from discrete and sparse observations. To address functional expressiblity, a key limitation of the black-box machine learning methods, we have exploited ...
    • Forward sensitivity approach for estimating eddy viscosity closures in nonlinear model reduction 

      Ahmed, Shady E; Bhar, Kinjal; San, Omer; Rasheed, Adil (Peer reviewed; Journal article, 2020)
      In this paper, we propose a variational approach to estimate eddy viscosity using forward sensitivity method (FSM) for closure modeling in nonlinear reduced order models. FSM is a data assimilation technique that blends ...
    • GANs enabled super-resolution reconstruction of wind field 

      Tran, Duy Tan; Robinson, Haakon; Rasheed, Adil; San, Omer; Tabib, Mandar; Kvamsdal, Trond (Peer reviewed; Journal article, 2020)
      Atmospheric flows are governed by a broad variety of spatio-temporal scales, thus making real-time numerical modeling of such turbulent flows in complex terrain at high resolution computationally unmanageable. In this ...
    • GANs enabled super-resolution reconstruction of wind field 

      Tran, Duy Tan; Robinson, Haakon; Rasheed, Adil; San, Omer; Kvamsdal, Trond (Peer reviewed; Journal article, 2020)
      Atmospheric flows are governed by a broad variety of spatio-temporal scales, thus making real-time numerical modeling of such turbulent flows in complex terrain at high resolution computationally unmanageable. In this ...
    • Geometric Change Detection in Digital Twins using 3D Machine Learning 

      Sundby, Tiril; Graham, Julia Maria; Rasheed, Adil; Tabib, Mandar; San, Omer (Peer reviewed; Journal article, 2021)
      Digital twins are meant to bridge the gap between real-world physical systems and virtual representations. Both stand-alone and descriptive digital twins incorporate 3D geometric models, which are the physical representations ...
    • Interface learning of multiphysics and multiscale systems 

      Ahmed, Shady E; San, Omer; Kara, Kursat; Younis, Rami; Rasheed, Adil (Peer reviewed; Journal article, 2020)
      Complex natural or engineered systems comprise multiple characteristic scales, multiple spatiotemporal domains, and even multiple physical closure laws. To address such challenges, we introduce an interface learning paradigm ...
    • A long short term memory for hybrid uplifted reduced order models. 

      Ahmed, Shady E; San, Omer; Rasheed, Adil; Trian, Iliescu (Peer reviewed; Journal article, 2020)
      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) embedding. Our approach has three modeling ...
    • Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows 

      Pawar, Suraj; Ahmed, Shady E; San, Omer; Rasheed, Adil; Navon, Ionel M (Peer reviewed; Journal article, 2020)
      Reduced rank nonlinear filters are increasingly utilized in data assimilation of geophysical flows but often require a set of ensemble forward simulations to estimate forecast covariance. On the other hand, predictor–corrector ...
    • 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, ...
    • A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction 

      Ahmed, Shady E; Pawar, Suraj; San, Omer; Rasheed, Adil; Tabib, Mandar (Journal article, 2020)
      We put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements for air traffic improvements. Toward emerging applications of ...
    • Physics guided machine learning using simplified theories 

      Pawar, Suraj; San, Omer; Aksoylu, Burak; Rasheed, Adil; Kvamsdal, Trond (Peer reviewed; Journal article, 2021)
      Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this Letter, we introduce a modular ...
    • A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence 

      Pawar, Suraj; San, Omer; Rasheed, Adil; Vedula, Prakash (Peer reviewed; Journal article, 2020)
      In the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the a priori settings. These models utilize resolved flow field variables on the coarser ...
    • Reduced order modeling of fluid flows: Machine learning, Kolmogorov barrier, closure modeling, and partitioning 

      Ahmed, Shady E; Pawar, Suraj; San, Omer; Rasheed, Adil (Peer reviewed; Journal article, 2020)
      In this paper, we put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements. We build on the fact that in a realistic ...