Brendan Tracey

Magnetic control of tokamak plasmas through deep reinforcement learning

Jonas Degrave, F. Felici, Jonas Buchli et al. · Nature · 2022 · 703 citations · Full text

Natural Sciences, Nuclear Engineering, Physics +16

A Machine Learning Strategy to Assist Turbulence Model Development

Brendan Tracey, Karthikeyan Duraisamy, Juan J. Alonso · 53rd AIAA Aerospace Sciences Meeting · 2015 · 289 citations · Full text

Natural Sciences, Computer Science, Mathematics +13

Stanford University Unstructured (SU2): Analysis and Design Technology for Turbulent Flows

Francisco Palacios, Thomas D. Economon, Aniket Aranake et al. · 52nd Aerospace Sciences Meeting · 2014 · 145 citations

Engineering, Aerospace Engineering, Mechanical Engineering +17

On the information bottleneck theory of deep learning*

Andrew Saxe, Yamini Bansal, Joel Dapello et al. · Journal of Statistical Mechanics Theory and Experiment · 2019 · 142 citations

Computing, Computer Science, Artificial Intelligence +12

Application of Supervised Learning to Quantify Uncertainties in Turbulence and Combustion Modeling

Brendan Tracey, Karthik Duraisamy, Juan J. Alonso · 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition · 2013 · 120 citations

Computing, Computational Engineering, Computer Science +14