5 citations · 13 across the 5 of their papers we have counts for
5 papers
Non-intrusive data-driven model order reduction for circuits based on Hammerstein architectures
Joshua Hanson, Paul Kuberry, Biliana Paskaleva +1
We demonstrate that system identification techniques can provide a basis for effective, non-intrusive model order reduction (MOR) for common circuits that are key building blocks i…
Fitting an immersed submanifold to data via Sussmann's orbit theorem
Joshua Hanson, Maxim Raginsky
This paper describes an approach for fitting an immersed submanifold of a finite-dimensional Euclidean space to random samples. The reconstruction mapping from the ambient space to…
Learning Recurrent Neural Net Models of Nonlinear Systems
Joshua Hanson, Maxim Raginsky, Eduardo Sontag
We consider the following learning problem: Given sample pairs of input and output signals generated by an unknown nonlinear system (which is not assumed to be causal or time-invar…
Learning Compact Physics-Aware Delayed Photocurrent Models Using Dynamic Mode Decomposition
Joshua Hanson, Pavel Bochev, Biliana Paskaleva
Radiation-induced photocurrent in semiconductor devices can be simulated using complex physics-based models, which are accurate, but computationally expensive. This presents a chal…
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets
Joshua Hanson, Maxim Raginsky
There has been a recent shift in sequence-to-sequence modeling from recurrent network architectures to convolutional network architectures due to computational advantages in traini…