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@MISC{Daniel:1045841,
      author       = {Daniel, Davis Thomas and Bartsch, Christian Hippolyt and
                      Bereck, Franz Philipp and Köcher, Simone Swantje and
                      Scheurer, Christoph and Granwehr, Josef},
      title        = {{ILT}py; 1.0.0},
      reportid     = {FZJ-2025-03628},
      year         = {2025},
      note         = {Licensed under LGPL},
      abstract     = {ILTpy (/ɪltˈpaɪ/) is a python library for performing
                      regularized inversion of one-dimensional or
                      multi-dimensional data without non-negativity constraint.
                      Contributions to respective distributions with both positive
                      and negative sign are determined. Primary applications
                      include magnetic resonance (NMR, EPR), and electrochemical
                      impedance spectroscopy (distribution of relaxation times;
                      DRT). ILTpy implements an inversion algorithm to fit
                      experimental or simulated noisy data of complex materials by
                      computing distributions of underlying physical or chemical
                      properties. It was initially developed for magnetic
                      resonance relaxation and diffusion data, and then also
                      utilized for electrochemical impedance (EIS) data. These
                      data often contain multiple components with varying
                      distributions. Fitting a specific model, such as a
                      mono-exponential, requires prior knowledge of the number of
                      species present and yields only an effective characteristic
                      constant. In contrast, inversion algorithms do not assume
                      the shape or number of species in the system, but instead
                      reveal the distribution of characteristic constants using a
                      kernel suitable for modeling the response of a particular
                      process. A common approach to analyzing magnetic resonance
                      data using Inverse Laplace Transform (ILT) methods involves
                      applying a non-negativity constraint to prevent oscillatory
                      solutions. This constraint assumes that all relaxation
                      components have the same sign. However, in systems where
                      cross-relaxation or exchange occurs, such a constraint is
                      unjustified, as it suppresses any relaxation components with
                      negative values, potentially introducing artificial features
                      in the resulting distributions that do not correspond to
                      actual physical processes. In contrast, ILTpy avoids the use
                      of a non-negativity constraint, employing instead a
                      zero-crossing penalty along with uniform penalty
                      regularization to stabilize the inversion process.},
      cin          = {IET-1},
      cid          = {I:(DE-Juel1)IET-1-20110218},
      pnm          = {1223 - Batteries in Application (POF4-122) / DFG project
                      G:(GEPRIS)422726248 - SPP 2248: Polymer-basierte Batterien
                      (422726248)},
      pid          = {G:(DE-HGF)POF4-1223 / G:(GEPRIS)422726248},
      typ          = {PUB:(DE-HGF)33},
      url          = {https://juser.fz-juelich.de/record/1045841},
}