most citedPrediction of Aerodynamic Flow Fields Using Convolutional Neural Networks

558 citations · 558 across the 1 of their papers we have counts for

collaborators

5 papers

math.DS2019

Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability

Shaowu Pan, Karthik Duraisamy

The Koopman operator has emerged as a powerful tool for the analysis of nonlinear dynamical systems as it provides coordinate transformations to globally linearize the dynamics. Wh…

physics.flu-dyn2019558 cited

Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networks

Yaser Afshar, Saakaar Bhatnagar, Shaowu Pan +2

An approximation model based on convolutional neural networks (CNNs) is proposed for flow field predictions. The CNN is used to predict the velocity and pressure field in unseen fl…

math.DS2019

On the Structure of Time-delay Embedding in Linear Models of Non-linear Dynamical Systems

Shaowu Pan, Karthik Duraisamy

This work addresses fundamental issues related to the structure and conditioning of linear time-delayed models of non-linear dynamics on an attractor. While this approach has been…

stat.ML2018

Long-time predictive modeling of nonlinear dynamical systems using neural networks

Shaowu Pan, Karthik Duraisamy

We study the use of feedforward neural networks (FNN) to develop models of nonlinear dynamical systems from data. Emphasis is placed on predictions at long times, with limited data…

math.DS2018

Data-driven Discovery of Closure Models

Shaowu Pan, Karthik Duraisamy

Derivation of reduced order representations of dynamical systems requires the modeling of the truncated dynamics on the retained dynamics. In its most general form, this so-called…