LaPON: A Lagrange's-mean-value-theorem-inspired operator network for solving PDEs and its application on NSE

TL;DR

LaPON is a hybrid neural operator network inspired by the Lagrange's mean value theorem that embeds physical constraints directly into its architecture, enabling accurate, stable, and generalizable solutions to PDEs like the Navier-Stokes equations at coarser resolutions.

Contribution

This work introduces LaPON, a novel operator network that incorporates prior physical knowledge directly into its structure, improving PDE solving efficiency and accuracy over existing methods.

Findings

Outperforms baseline methods on turbulence simulations at coarser grids and larger time steps.

Achieves over 0.98 vorticity correlation with ground truth.

Generalizes well to unseen flow conditions without retraining.

Abstract

Accelerating the solution of nonlinear partial differential equations (PDEs) while maintaining accuracy at coarse spatiotemporal resolution remains a key challenge in scientific computing. Physics-informed machine learning (ML) methods such as Physics-Informed Neural Networks (PINNs) introduce prior knowledge through loss functions to ensure physical consistency, but their "soft constraints" are usually not strictly satisfied. Here, we propose LaPON, an operator network inspired by the Lagrange's mean value theorem, which embeds prior knowledge directly into the neural network architecture instead of the loss function, making the neural network naturally satisfy the given constraints. This is a hybrid framework that combines neural operators with traditional numerical methods, where neural operators are used to compensate for the effect of discretization errors on the analytical scale in under-resolution simulations. As evaluated on turbulence problem modeled by the Navier-Stokes equations (NSE), the multiple time step extrapolation accuracy and stability of LaPON exceed the direct numerical simulation baseline at 8x coarser grids and 8x larger time steps, while achieving a vorticity correlation of more than 0.98 with the ground truth. It is worth noting that the model can be well generalized to unseen flow states, such as turbulence with different forcing, without retraining. In addition, with the same training data, LaPON's comprehensive metrics on the out-of-distribution test set are at least approximately twice as good as two popular ML baseline methods. By combining numerical computing with machine learning, LaPON provides a scalable and reliable solution for high-fidelity fluid dynamics simulation, showing the potential for wide application in fields such as weather forecasting and engineering design.