Interface PINNs (I-PINNs): A physics-informed neural networks framework for interface problems

Sarma, Antareep Kumar; Roy, Sumanta; Annavarapu, Chandrasekhar; Roy, Pratanu; Jagannathan, Shriram

doi:10.1016/j.cma.2024.117135

Interface PINNs (I-PINNs): A physics-informed neural networks framework for interface problems

Journal Article · Thu Jun 20 00:00:00 EDT 2024 · Computer Methods in Applied Mechanics and Engineering

DOI:https://doi.org/10.1016/j.cma.2024.117135· OSTI ID:2425908

^[1]; ^[1]; ^[1]; ^[2]; Jagannathan, Shriram ^[3]

Indian Inst. of Technology (IIT), Madras (India)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Nvidia Corporation, Santa Clara, CA (United States)

Here, we present a novel physics-informed neural networks (PINNs) framework for modeling interface problems, termed Interface PINNs (I-PINNs). I-PINNs uses different neural networks for any two subdomains separated by a sharp interface such that the neural networks differ only through their activation functions while the other parameters remain identical. The performance of I-PINNs, conventional PINNs, and other existing domain-decomposition PINNs methods such as extended PINNs (XPINNs) and multi-domain PINN (M-PINN) is compared through several one-dimensional, two-dimensional, and three-dimensional benchmark elliptic interface problems. The results demonstrate that I-PINNs provides a root-mean-square-error accuracy, at least two orders of magnitude better than conventional PINNs and XPINNs at approximately one-tenth of the computational cost of conventional PINNs and half the cost of XPINNs. Additionally, while I-PINNs and M-PINN provide comparable accuracies, M-PINN is found to be approximately 50% more expensive.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 2425908

Report Number(s):: LLNL--JRNL-863829; 1096935

Journal Information:: Computer Methods in Applied Mechanics and Engineering, Journal Name: Computer Methods in Applied Mechanics and Engineering Vol. 429; ISSN 0045-7825

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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