3 citations · 3 across the 6 of their papers we have counts for
7 papers
Stable Rational Approximation of PDE Transfer Functions with Error Bounds
Aleksandr Talitckii, Matthew M. Peet
Transfer functions of Partial Differential Equations (PDEs) are irrational and difficult to obtain. Unlike for rational transfer functions, only a few methods are available for the…
A -Analysis and Synthesis Framework for Partial Integral Equations using IQCs
Thijs Lenssen, Aleksandr Talitckii, Matthew Peet +1
We develop a -analysis and synthesis framework for infinite-dimensional systems that leverages the Integral Quadratic Constraints (IQCs) to compute the structured singular value…
Picard Iteration for Parameter Estimation in Nonlinear Ordinary Differential Equations using Low-Quality Data
Aleksandr Talitckii, Matthew M. Peet
We consider the problem of using experimental time-series data for parameter estimation in nonlinear ordinary differential equations, focusing on the case where the data is noisy,…
A Computational Method for -optimal Estimator and State Feedback Controller Synthesis for PDEs
Sachin Shivakumar, Matthew Peet
In this paper, we present solvable, convex formulations of -optimal state estimation and state-feedback control problems for a general class of linear Partial Differential Equ…
Representation of PDE Systems with Delay and Stability Analysis using Convex Optimization -- Extended Version
Declan S. Jagt, Matthew M. Peet
Partial Integral Equations (PIEs) have been used to represent both systems with delay and systems of Partial Differential Equations (PDEs) in one or two spatial dimensions. In this…
Employing Feature Selection Algorithms to Determine the Immune State of a Mouse Model of Rheumatoid Arthritis
Brendon K. Colbert, Joslyn L. Mangal, Aleksandr Talitckii +2
The immune response is a dynamic process by which the body determines whether an antigen is self or nonself. The state of this dynamic process is defined by the relative balance an…
A Convex Parametrization of a New Class of Universal Kernel Functions
Brendon K. Colbert, Matthew M. Peet
The accuracy and complexity of kernel learning algorithms is determined by the set of kernels over which it is able to optimize. An ideal set of kernels should: admit a linear para…