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ModalPINN: an extension of Physics-Informed Neural Networks with enforced truncated Fourier decomposition for periodic flow reconstruction using a limited number of imperfect sensors
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TLDR
ModalPINN as mentioned in this paper encodes the approximation of a limited number of Fourier mode shapes and performs up to two orders of magnitude more precisely for a similar number of degrees of freedom and training time in some cases as illustrated through the laminar shedding of vortices over a cylinder.Abstract:
Continuous reconstructions of periodic phenomena provide powerful tools to understand, predict and model natural situations and engineering problems. In line with the recent method called Physics-Informed Neural Networks (PINN) where a multi layer perceptron directly approximates any physical quantity as a symbolic function of time and space coordinates, we present an extension, namely ModalPINN, that encodes the approximation of a limited number of Fourier mode shapes. In addition to the added interpretability, this representation performs up to two orders of magnitude more precisely for a similar number of degrees of freedom and training time in some cases as illustrated through the test case of laminar shedding of vortices over a cylinder. This added simplicity proves to be robust in regards to flow reconstruction using only a limited number of sensors with asymmetric data that simulates an experimental configuration, even when a Gaussian noise or a random delay is added, imitating imperfect and sparse information.read more
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Physics-Informed Neural Network (PINN) Evolution and Beyond: A Systematic Literature Review and Bibliometric Analysis
TL;DR: In this article, a review of state-of-the-art physics-informed neural networks (PINNs) from different researchers' perspectives is presented, where newly improved techniques developed to enhance PINN performance and reduce high training costs and slowness, among other limitations, are highlighted.
Journal ArticleDOI
A stepwise physics‐informed neural network for solving large deformation problems of hypoelastic materials
TL;DR: In this article , a stepwise physics-informed neural network (sPINN) is proposed to solve large deformation problems of hypoelastic materials, where the whole process of sPINN can be divided into a series of time steps, and the rate constitutive equation expressed by Hughes-Winget algorithm and momentum governing equation are incorporated into the loss function as physical constraints.
References
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