72 citations · 363 across the 68 of their papers we have counts for
4 papers · 1 filter
Doing the impossible: Why neural networks can be trained at all
Nathan O. Hodas, Panos Stinis
As deep neural networks grow in size, from thousands to millions to billions of weights, the performance of those networks becomes limited by our ability to accurately train them.…
Renormalization and blow-up for the 3D Euler equations
Jacob Price, Panos Stinis
In recent work we have developed a renormalization framework for stabilizing reduced order models for time-dependent partial differential equations. We have applied this framework…
Mori-Zwanzig reduced models for uncertainty quantification
Jing Li, Panos Stinis
In many time-dependent problems of practical interest the parameters and/or initial conditions entering the equations describing the evolution of the various quantities exhibit unc…
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks
Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky +1
We suggest ways to enforce given constraints in the output of a Generative Adversarial Network (GAN) generator both for interpolation and extrapolation (prediction). For the case o…