Fast-PINN for Complex Geometry: Solving PDEs with Boundary Connectivity Loss

Jian Cheng Wong, Pao-Hsiung Chiu, Chin Chun Ooi, My Ha Dao, Yew-Soon Ong

Keywords: Physics-informed neural networks, physics-informed loss formulation, multi-layer perceptron, convolutional neural network, fluid dynamics

TL;DR: We present a fast-PINN method based on the incorporation of boundary connectivity constraints into training loss, which can efficiently produce accurate solutions with order of magnitude fewer training samples, across multiple fluid dynamic problems.

Abstract: We present a novel loss formulation for efficient learning of complex dynamics from governing physics, typically described by partial differential equations (PDEs), using physics-informed neural networks (PINNs). In our experiments, existing versions of PINNs are seen to learn poorly in many problems, especially for complex geometries, as it becomes increasingly difficult to establish appropriate sampling strategy at the near boundary region. Overly dense sampling can adversely impede training convergence if the local gradient behaviors are too complex to be adequately modelled by PINNs. On the other hand, if the samples are too sparse, PINNs may over-fit the near boundary region, leading to incorrect solution. To prevent such issues, we propose a new Boundary Connectivity (BCXN) loss function which provides local structure approximation at the boundary. Our BCXN-loss can implicitly or explicitly impose such approximations during training, thus facilitating fast physics-informed learning across entire problem domains with order of magnitude fewer training samples. This method shows a few orders of magnitude smaller errors than existing methods in terms of the standard L2-norm metric, while using dramatically fewer training samples and iterations. Our proposed Fast-PINN method does not pose any requirement on the differentiable property of the networks, and we demonstrate its benefits and ease of implementation on both multi-layer perceptron and convolutional neural network versions as commonly used in current physics-informed neural network literature.

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