@Article{CiCP-37-3,
author = {Sumanta, Roy and Chandrasekhar, Annavarapu and Roy, Pratanu and Sarma, Kumar, Antareep},
title = {Adaptive Interface-PINNs (AdaI-PINNs): An Efficient Physics-Informed Neural Networks Framework for Interface Problems},
journal = {Communications in Computational Physics},
year = {2025},
volume = {37},
number = {3},
pages = {603--622},
abstract = {<p style="text-align: justify;">We present an efficient physics-informed neural networks (PINNs) framework, termed Adaptive Interface-PINNs (AdaI-PINNs), to improve the modeling of
interface problems with discontinuous coefficients and/or interfacial jumps. This framework is an enhanced version of its predecessor, Interface PINNs or I-PINNs (Sarma et
al. [1]; https://doi.org/10.1016/j.cma.2024.117135), which involves domain decomposition and assignment of different predefined activation functions to the neural networks in each subdomain across a sharp interface, while keeping all other parameters
of the neural networks identical. In AdaI-PINNs, the activation functions vary solely in
their slopes, which are trained along with the other parameters of the neural networks.
This makes the AdaI-PINNs framework fully automated without requiring preset activation functions. Comparative studies on one-dimensional, two-dimensional, and
three-dimensional benchmark elliptic interface problems reveal that AdaI-PINNs outperform I-PINNs, reducing computational costs by 2-6 times while producing similar
or better accuracy.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2024-0131},
url = {https://global-sci.com/article/91725/adaptive-interface-pinns-adai-pinns-an-efficient-physics-informed-neural-networks-framework-for-interface-problems}
}