Abstract
AbstractPhysics‐Informed Neural Networks (PINNs) are a novel discretization scheme for the solution of partial differential equations (PDEs), where a neural network is chosen as global ansatz function. The problem of solving the PDE is then cast as an optimization problem and addressed by training the neural network. PINNs have been promoted to perform particularly well in inverse problems. This contribution presents recent advances of the Neural Particle Method, an updated Lagrangian Physics‐Informed Neural Network, in the inverse problem of reconstructing flow fields from sparse data.
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