51 citations · 140 across the 11 of their papers we have counts for
11 papers
Simultaneous Identification and Denoising of Dynamical Systems
Jeffrey M. Hokanson, Gianluca Iaccarino, Alireza Doostan
In recent years there has been a push to discover the governing equations dynamical systems directly from measurements of the state, often motivated by systems that are too complex…
An integrated heterogeneous computing framework for ensemble simulations of laser-induced ignition
Kazuki Maeda, Thiago Teixeira, Jonathan M. Wang +6
An integrated computational framework is introduced to study complex engineering systems through physics-based ensemble simulations on heterogeneous supercomputers. The framework i…
Multivariate Rational Approximation Using a Stabilized Sanathanan-Koerner Iteration
Jeffrey M. Hokanson
The Sanathanan-Koerner iteration developed in 1963 is classical approach for rational approximation. This approach multiplies both sides of the approximation by the denominator pol…
A Numerical Investigation of the Minimum Width of a Neural Network
Ibrohim Nosirov, Jeffrey M. Hokanson
Neural network width and depth are fundamental aspects of network topology. Universal approximation theorems provide that with increasing width or depth, there exists a neural netw…
A Lipschitz Matrix for Parameter Reduction in Computational Science
Jeffrey M. Hokanson, Paul G. Constantine
We introduce the Lipschitz matrix: a generalization of the scalar Lipschitz constant for functions with many inputs. Among the Lipschitz matrices compatible a particular function,…
Least Squares Rational Approximation
Jeffrey M. Hokanson, Caleb C. Magruder
Rational approximation appears in many contexts throughout science and engineering, playing a central role in linear systems theory, special function approximation, and many others…
-Optimal Model Reduction Using Projected Nonlinear Least Squares
Jeffrey M. Hokanson, Caleb C. Magruder
In many applications throughout science and engineering, model reduction plays an important role replacing expensive large-scale linear dynamical systems by inexpensive reduced ord…
A Data-Driven McMillan Degree Lower Bound
Jeffrey M. Hokanson
Given measurements of a linear time-invariant system, the McMillan degree is the dimension of the smallest such system that reproduces these observed dynamics. Using impulse respon…
Data-driven polynomial ridge approximation using variable projection
Jeffrey M. Hokanson, Paul G. Constantine
Inexpensive surrogates are useful for reducing the cost of science and engineering studies involving large-scale, complex computational models with many input parameters. A ridge a…
A near-stationary subspace for ridge approximation
Paul G. Constantine, Armin Eftekhari, Jeffrey Hokanson +1
Response surfaces are common surrogates for expensive computer simulations in engineering analysis. However, the cost of fitting an accurate response surface increases exponentiall…
Projected nonlinear least squares for exponential fitting
Jeffrey M. Hokanson
The modern ability to collect vast quantities of data poses a challenge for parameter estimation problems. When posed as a nonlinear least squares problem fitting a model to data,…