A Physics Informed Neural Network (PINN) Methodology for Coupled Moving Boundary PDEs
<|reference_start|>A Physics Informed Neural Network (PINN) Methodology for Coupled Moving Boundary PDEs: Physics-Informed Neural Network (PINN) is a novel multi-task learning framework useful for solving physical problems modeled using differential equations (DEs) by integrating the knowledge of physics and known constraints into the components of deep learning. A large class of physical problems in materials science and mechanics involve moving boundaries, where interface flux balance conditions are to be satisfied while solving DEs. Examples of such systems include free surface flows, shock propagation, solidification of pure and alloy systems etc. While recent research works have explored applicability of PINNs for an uncoupled system (such as solidification of pure system), the present work reports a PINN-based approach to solve coupled systems involving multiple governing parameters (energy and species, along with multiple interface balance equations). This methodology employs an architecture consisting of a separate network for each variable with a separate treatment of each phase, a training strategy which alternates between temporal learning and adaptive loss weighting, and a scheme which progressively reduces the optimisation space. While solving the benchmark problem of binary alloy solidification, it is distinctly successful at capturing the complex composition profile, which has a characteristic discontinuity at the interface and the resulting predictions align well with the analytical solutions. The procedure can be generalised for solving other transient multiphysics problems especially in the low-data regime and in cases where measurements can reveal new physics.<|reference_end|>
@article{kathane2024a,
    title={A Physics Informed Neural Network (PINN) Methodology for Coupled Moving
  Boundary PDEs},
    author={Shivprasad Kathane and Shyamprasad Karagadde (Indian Institute of
  Technology Bombay Mumbai India)},
    journal={arXiv preprint arXiv:2409.10910},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10910},
    primaryClass={cs.LG math.AP}
}