Revolutionizing fluid flow with Lyapunov exponents and PDFP optimizer based Physics Informed Neural Networks for microrotation and peristaltic transport

Muhammad Asaad, Muhammad Israr, Muhammad Asif, Hamiden Abd El-Wahed Khalifa, Norah Alhunayshil and Assad Ayub

Chaos, Solitons & Fractals, 2026, vol. 209, issue P1

Abstract: The Lyapunov Exponent shows stability and chaos of dynamical systems. In fluid mechanics, it helps to quantify the sensitivity of fluid flows to initial conditions, making it essential for optimizing complex systems such as peristaltic transport and micro rotational flows. So, this study is important because it solves involved Partial Differential Equations (PDEs) with data efficient computational tool of Physics Informed Neural Network (PINNs).

Keywords: Lyapunov exponents; Nanofluidics; Numerical results; PDFP optimizer algorithms; PINN with multiple hidden layers (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:209:y:2026:i:p1:s0960077926005333

DOI: 10.1016/j.chaos.2026.118392

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