% Encoding: ISO8859_1

@Article{A:24,
      title={Global optimality conditions for sensor placement, with extensions to binary A-optimal experimental designs}, 
      author={Christian Aarset},
      year={2024},
      eprint={2410.16590},
      archivePrefix={arXiv},
      primaryClass={math.OC},
      url={https://arxiv.org/abs/2410.16590}
}

@Article{AHHM:24,
  author={Aarset, Christian and Habring, Andreas and Holler, Martin and Mitter, Mario},
  journal={IEEE Transactions on Consumer Electronics}, 
  title={Unsupervised Energy Disaggregation Via Convolutional Sparse Coding}, 
  year={2024},
  volume={70},
  number={1},
  pages={4303-4310},
  doi={10.1109/TCE.2023.3324921}
}

@Article{NAH:23,
author={Aarset, Christian
and Holler, Martin
and Nguyen, Tram Thi Ngoc},
title={Learning-Informed Parameter Identification in Nonlinear Time-Dependent PDEs},
journal={Applied Mathematics {\&} Optimization},
year={2023},
month={Aug},
day={23},
volume={88},
number={3},
pages={76},
abstract={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 formulated in a rather general setting with three unknowns: physical parameter, state and nonlinearity. Inspired by advances in machine learning, we approximate the nonlinearity via a neural network, whose parameters are learned from measurement data. The latter is assumed to be given as noisy observations of the unknown state, and both the state and the physical parameters are identified simultaneously with the parameters of the neural network. Moreover, diverging from the classical approach, the proposed all-at-once setting avoids constructing the parameter-to-state map by explicitly handling the state as additional variable. The practical feasibility of the proposed method is confirmed with experiments using two different algorithmic settings: A function-space algorithm based on analytic adjoints as well as a purely discretized setting using standard machine learning algorithms.},
issn={1432-0606},
doi={10.1007/s00245-023-10044-y},
url={https://doi.org/10.1007/s00245-023-10044-y}
}

@Article{AP:21,
author = {Christian Aarset, Christian Pötzsche},
title = {Bifurcations in periodic integrodifference equations in C(Ω) I: Analytical results and applications},
journal = {Discrete & Continuous Dynamical Systems – B},
year = {2021}, 
volume = {26},
number = {1},
pages = {1--60},
doi = {10.3934/dcdsb.2020231},
url = {https://arxiv.org/abs/2006.14406}
}

@Article{AP:20,
author = {Christian Aarset, Christian Pötzsche},
title = {Bifurcations in periodic integrodifference equations in C(Ω) II: Discrete torus bifurcations},
journal = {Communications on Pure & Applied Analysis},
year = {2020}, 
volume = {19},
number = {4},
pages = {1847-1874,},
doi = {10.3934/cpaa.2020081},
url = {https://www.researchgate.net/publication/337892861_Bifurcations_in_periodic_integrodifference_equations_in_CO_II_Discrete_torus_bifurcations}
}