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David Sondak

Hamiltonian neural networks for solving equations of motion
2022 · Marios Mattheakis, David Sondak, et al. · Physical review. E · 152
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NeuroDiffEq: A Python package for solving differential equations with neural networks
2020 · Feiyu Chen, David Sondak, et al. · The Journal of Open Source Software · 140
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Port-Hamiltonian neural networks for learning explicit time-dependent dynamical systems
2021 · Shaan A. Desai, Marios Mattheakis, et al. · Physical review. E · 87
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Neural network models for the anisotropic Reynolds stress tensor in turbulent channel flow
2019 · Rui Fang, David Sondak, et al. · Journal of Turbulence · 55
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Solving Differential Equations Using Neural Network Solution Bundles
2020 · Flamant, Cedric, Protopapas, Pavlos, et al. · arXiv (Cornell University) · 48
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Optimal heat transport solutions for Rayleigh–Bénard convection
2015 · David Sondak, Leslie M. Smith, et al. · Journal of Fluid Mechanics · 42
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Hamiltonian Neural Networks for Solving Differential Equations
2020 · Mattheakis, Marios, Sondak, David, et al. · 21
Deep learning for turbulent channel flow
2018 · Fang, Rui, Sondak, David, et al. · arXiv (Cornell University) · 21
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Can phoretic particles swim in two dimensions?
2016 · David Sondak, Cory Hawley, et al. · Physical review. E · 20
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A new class of finite element variational multiscale turbulence models for incompressible magnetohydrodynamics
2015 · D. Sondak, J.N. Shadid, et al. · Journal of Computational Physics · 20
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Coherent solutions and transition to turbulence in two-dimensional Rayleigh-Bénard convection
2021 · Parvathi Kooloth, David Sondak, et al. · Physical Review Fluids · 19
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A residual based eddy viscosity model for the large eddy simulation of turbulent flows
2014 · A.A. Oberai, J. Liu, et al. · Computer Methods in Applied Mechanics and Engineering · 18
Convolutional neural network models and interpretability for the anisotropic reynolds stress tensor in turbulent one-dimensional flows
2021 · Haitz Sáez de Ocáriz Borde, David Sondak, et al. · Journal of Turbulence · 16
DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks
2022 · Bullwinkel, Blake, Randle, Dylan, et al. · arXiv (Cornell University) · 15
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Extending a Physics-informed Machine-learning Network for Superresolution Studies of Rayleigh–Bénard Convection
2024 · Diane M. Salim, Blakesley Burkhart, et al. · The Astrophysical Journal · 11
Large eddy simulation models for incompressible magnetohydrodynamics derived from the variational multiscale formulation
2012 · David Sondak, Assad A. Oberai · Physics of Plasmas · 9
Application of the variational Germano identity to the variational multiscale formulation
2010 · A. A. Oberai, D. Sondak · International Journal for Numerical Methods in Biomedical Engineering · 9
Learning a reduced basis of dynamical systems using an autoencoder
2021 · David Sondak, Pavlos Protopapas · Physical review. E · 8
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High Rayleigh number variational multiscale large eddy simulations of Rayleigh-Bénard convection
2020 · David Sondak, Thomas M. Smith, et al. · Mechanics Research Communications · 6
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Unsupervised Learning of Solutions to Differential Equations with Generative Adversarial Networks
2020 · Randle, Dylan, Protopapas, Pavlos, et al. · arXiv (Cornell University) · 6
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Finding multiple solutions of odes with neural networks
2020 · Marco Di Giovanni, David Sondak, et al. · Virtual Community of Pathological Anatomy (University of Castilla La Mancha) · 5
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Recent Advances of NeuroDiffEq -- An Open-Source Library for Physics-Informed Neural Networks
2025 · Liu, Shuheng, Protopapas, Pavlos, et al. · arXiv (Cornell University) · 4
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Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow
2021 · Borde, Haitz Sáez de Ocáriz, Sondak, David, et al. · arXiv (Cornell University) · 3
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Machine Learning-Based Prediction of Wind-Induced Interior Noise in Ground Vehicles
2026 · John Higgins, Nicolas Fougere, et al. · SAE technical papers on CD-ROM/SAE technical paper series · 2
An Inadequacy Formulation for an Uncertain Flamelet Model
2017 · David Sondak, Todd Oliver, et al. · AIAA Non-Deterministic Approaches Conference · 2
Works count: 47
Citation count: 747
H-index: 14
I10-index: 15
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