14 citations · 19 across the 7 of their papers we have counts for
7 papers
Data assimilation via model reference adaptation for linear and nonlinear dynamical systems
Benedikt Kaltenbach, Christian Aarset, Tram Thi Ngoc Nguyen
We address data assimilation for linear and nonlinear dynamical systems via the so-called model reference adaptive system. Continuing our theoretical developments, we deliver the f…
FEM-based A-optimal sensor placement for heat source inversion from final time measurement
Christian Aarset, Tram Thi Ngoc Nguyen
Within the field of optimal experimental design, \emph{sensor placement} refers to the act of finding the optimal locations of data collecting sensors, with the aim to optimise rec…
A global optimum-informed greedy algorithm for A-optimal experimental design
Christian Aarset
Optimal experimental design (OED) concerns itself with identifying ideal methods of data collection, e.g.~via sensor placement. The \emph{greedy algorithm}, that is, placing one se…
Bi-level regularization via iterative mesh refinement for aeroacoustics
Christian Aarset, Tram Thi Ngoc Nguyen
In this work, we illustrate the connection between adaptive mesh refinement for finite element discretized PDEs and the recently developed \emph{bi-level regularization algorithm}.…
Global optimality conditions for sensor placement, with extensions to binary low-rank A-optimal designs
Christian Aarset
The \emph{sensor placement problem} for stochastic linear inverse problems consists of determining the optimal manner in which sensors can be employed to collect data. Specifically…
Unsupervised energy disaggregation via convolutional sparse coding
Christian Aarset, Andreas Habring, Martin Holler +1
In this work, a method for unsupervised energy disaggregation in private households equipped with smart meters is proposed. This method aims to classify power consumption as active…
Learning-informed parameter identification in nonlinear time-dependent PDEs
Christian Aarset, Martin Holler, Tram Thi Ngoc Nguyen
We introduce and analyze a method of learning-informed parameter identification for partial differential equations (PDEs) in an all-at-once framework. The underlying PDE model is f…