@misc{9244270,
  abstract     = {{Unsaturated soil water flow is commonly described by Richards’ equation, a nonlinear PDE that is typically solved using numerical methods. In this thesis, two alternative physics-informed machine learning approaches are investigated for forward and inverse modeling of unsaturated flow. First, physics-informed neural networks (PINNs) are applied to approximate solutions of Richards’ equation in a one-dimensional homogeneous soil setting. Their performance is evaluated by comparison with numerical reference solutions. In addition, an inverse PINN
framework is employed to estimate the soil hydraulic parameters α, n and the saturated hydraulic conductivity Ks of the van Genuchten model. Second, a physics-informed deep operator network (DeepONet) is employed to learn the mapping between varying upper boundary flux functions and the corresponding solutions of Richards’ equation. In this context, the model’s ability to generalize across different boundary conditions is assessed.}},
  author       = {{Grimm, Jule}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Physics-Informed Machine Learning for Modeling Unsaturated Soil Water Flow}},
  year         = {{2026}},
}