2 citations · 2 across the 6 of their papers we have counts for
6 papers
Inertial Manifold Neural Operator for Dissipative Time-Dependent Partial Differential Equations
Xiaoyang Xie, Clarence W. Rowley
In this paper, we introduce the Inertial Manifold Neural Operator (IMNO) for solving dissipative time-dependent partial differential equations (PDEs). The long-time dynamics of suc…
Symmetry-reduced model reduction of shift-equivariant systems via operator inference
Yu Shuai, Clarence W. Rowley
We consider data-driven reduced-order models of partial differential equations with shift equivariance. Shift-equivariant systems typically admit traveling solutions, and the main…
Resolvent4py: a parallel Python package for analysis, model reduction and control of large-scale linear systems
Alberto Padovan, Vishal Anantharaman, Clarence W. Rowley +3
In this paper, we present resolvent4py, a parallel Python package for the analysis, model reduction and control of large-scale linear systems with millions or billions of degrees o…
Almost Optimal Agnostic Control of Unknown Linear Dynamics
Jacob Carruth, Maximilian F. Eggl, Charles Fefferman +1
We consider a simple control problem in which the underlying dynamics depend on a parameter that is unknown and must be learned. We study three variants of the control problem:…
Controlling Unknown Linear Dynamics with Almost Optimal Regret
Jacob Carruth, Maximilian F. Eggl, Charles Fefferman +1
Here and in a companion paper, we consider a simple control problem in which the underlying dynamics depend on a parameter that is unknown and must be learned. In this paper, w…
Optimal Agnostic Control of Unknown Linear Dynamics in a Bounded Parameter Range
Jacob Carruth, Maximilian F. Eggl, Charles Fefferman +1
Here and in a follow-on paper, we consider a simple control problem in which the underlying dynamics depend on a parameter that is unknown and must be learned. In this paper, w…