publications

2009

1. Optimal tuning of tokamak plasma equilibrium controllers in the presence of time delays

   Eugenio Schuster, David Sondak, Reza Arastoo, and 2 more authors

   In 2009 IEEE Control Applications,(CCA) & Intelligent Control,(ISIC), 2009

2011

1. Remediation of time-delay effects in tokamak axisymmetric control loops by optimal tuning and robust predictor augmentation

   D Sondak, R Arastoo, E Schuster, and 1 more author

   Fusion engineering and design, 2011
2. Application of the variational Germano identity to the variational multiscale formulation

   AA Oberai, and D Sondak

   International Journal for Numerical Methods in Biomedical Engineering, 2011

2012

1. Large eddy simulation models for incompressible magnetohydrodynamics derived from the variational multiscale formulation

   David Sondak, and Assad A Oberai

   Physics of Plasmas, 2012

2014

1. A residual based eddy viscosity model for the large eddy simulation of turbulent flows

   Assad A Oberai, J Liu, David Sondak, and 1 more author

   Computer Methods in Applied Mechanics and Engineering, 2014

2015

1. Optimal heat transport solutions for Rayleigh–Bénard convection

   David Sondak, Leslie M Smith, and Fabian Waleffe

   Journal of Fluid Mechanics, 2015
2. A new class of finite element variational multiscale turbulence models for incompressible magnetohydrodynamics

   David Sondak, John N Shadid, Assad A Oberai, and 3 more authors

   Journal of Computational Physics, 2015

2016

1. Can phoretic particles swim in two dimensions?

   David Sondak, Cory Hawley, Siyu Heng, and 3 more authors

   Physical Review E, 2016

2017

1. Drekar v. 2.0

   Ben Seefeldt, David Sondak, David M Hensinger, and 8 more authors

   2017
2. Algorithmic aspects and performance of AMG-based preconditioning for an implicit FE VMS resistive MHD model.

   Paul Lin, John N Shadid, Edward Geoffrey Phillips, and 5 more authors

   2017
3. An inadequacy formulation for an uncertain flamelet model

   David Sondak, Todd Oliver, Chris Simmons, and 1 more author

   In 19th AIAA Non-Deterministic Approaches Conference, 2017

2018

1. Deep learning for turbulent channel flow

   Rui Fang, David Sondak, Pavlos Protopapas, and 1 more author

   arXiv preprint arXiv:1812.02241, 2018

2019

1. Physical symmetries embedded in neural networks

   Marios Mattheakis, Pavlos Protopapas, David Sondak, and 2 more authors

   arXiv preprint arXiv:1904.08991, 2019

2020

1. Neural network models for the anisotropic Reynolds stress tensor in turbulent channel flow

   Rui Fang, David Sondak, Pavlos Protopapas, and 1 more author

   Journal of Turbulence, 2020
2. Unsupervised learning of solutions to differential equations with generative adversarial networks

   Dylan Randle, Pavlos Protopapas, and David Sondak

   arXiv preprint arXiv:2007.11133, 2020
3. Solving differential equations using neural network solution bundles

   Cedric Flamant, Pavlos Protopapas, and David Sondak

   arXiv preprint arXiv:2006.14372, 2020
4. Neurodiffeq: A python package for solving differential equations with neural networks

   Feiyu Chen, David Sondak, Pavlos Protopapas, and 4 more authors

   Journal of Open Source Software, 2020
5. Finding multiple solutions of odes with neural networks

   Marco Di Giovanni, David Sondak, Pavlos Protopapas, and 2 more authors

   In Combining Artificial Intelligence and Machine Learning with Physical Sciences 2020, 2020

2021

1. Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow

   Haitz Sáez Ocáriz Borde, David Sondak, and Pavlos Protopapas

   CoRR, 2021
2. Port-Hamiltonian neural networks for learning explicit time-dependent dynamical systems

   Shaan A Desai, Marios Mattheakis, David Sondak, and 2 more authors

   Physical Review E, 2021
3. Learning a reduced basis of dynamical systems using an autoencoder

   David Sondak, and Pavlos Protopapas

   Physical Review E, 2021
4. High Rayleigh number variational multiscale large eddy simulations of Rayleigh-Bénard convection

   David Sondak, Thomas M Smith, Roger P Pawlowski, and 2 more authors

   Mechanics Research Communications, 2021
5. Coherent solutions and transition to turbulence in two-dimensional Rayleigh-Bénard convection

   Parvathi Kooloth, David Sondak, and Leslie M Smith

   Physical Review Fluids, 2021
6. Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow

   Haitz Sáez Ocáriz Borde, David Sondak, and Pavlos Protopapas

   CoRR, 2021

2022

1. Deqgan: Learning the loss function for pinns with generative adversarial networks

   Blake Bullwinkel, Dylan Randle, Pavlos Protopapas, and 1 more author

   arXiv preprint arXiv:2209.07081, 2022
2. Auto-eD: A visual learning tool for automatic differentiation

   Lindsey S Brown, Rachel Moon, and David Sondak

   Journal of Open Source Education, 2022
3. Hamiltonian neural networks for solving equations of motion

   Marios Mattheakis, David Sondak, Akshunna S Dogra, and 1 more author

   Physical Review E, 2022
4. Convolutional neural network models and interpretability for the anisotropic reynolds stress tensor in turbulent one-dimensional flows

   Haitz Ocáriz Borde, David Sondak, and Pavlos Protopapas

   Journal of Turbulence, 2022