David Sondak

Orcid: 0000-0002-2730-9097

According to our database1, David Sondak authored at least 11 papers between 2009 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2022
DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks.
CoRR, 2022

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

Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems.
CoRR, 2021

2020
NeuroDiffEq: A Python package for solving differential equations with neural networks.
J. Open Source Softw., 2020

Unsupervised Learning of Solutions to Differential Equations with Generative Adversarial Networks.
CoRR, 2020

Solving Differential Equations Using Neural Network Solution Bundles.
CoRR, 2020

Hamiltonian Neural Networks for solving differential equations.
CoRR, 2020

Finding Multiple Solutions of ODEs with Neural Networks.
Proceedings of the AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 23rd - to, 2020

2019
Physical Symmetries Embedded in Neural Networks.
CoRR, 2019

2015
A new class of finite element variational multiscale turbulence models for incompressible magnetohydrodynamics.
J. Comput. Phys., 2015

2009
Optimal tuning of tokamak plasma equilibrium controllers in the presence of time delays.
Proceedings of the IEEE International Conference on Control Applications, 2009


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