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arXiv · 2308.11133

Learning the solution operator of a nonlinear parabolic equation using physics informed deep operator network

Daniel Sevcovic·Cyril Izuchukwu Udeani
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Abstract

This study focuses on addressing the challenges of solving analytically intractable differential equations that arise in scientific and engineering fields such as Hamilton-Jacobi-Bellman. Traditional numerical methods and neural network approaches for solving such equations often require independent simulation or retraining when the underlying parameters change. To overcome this, this study employs a physics-informed DeepONet (PI-DeepONet) to approximate the solution operator of a nonlinear parabolic equation. PI-DeepONet integrates known physics into a deep neural network, which learns the solution of the PDE.

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Daniel Sevcovic, Cyril Izuchukwu Udeani. 2023-08-22. Learning the solution operator of a nonlinear parabolic equation using physics informed deep operator network. https://arxiv.org/abs/2308.11133

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