Numerical modeling of the propagation process of landslide surge using physics-informed deep learning

Abstract

The landslide surge is a common secondary disaster of reservoir bank landslides, which can cause more serious damage than the landslide itself in many cases. With the development of large-scale scientific and engineering computing, many new techniques have been applied to the study of hydrodynamic problems to make up for the shortcomings of traditional methods. In this paper, we use the physics-informed neural network (PINN) to simulate the propagation process of surges caused by landslides. We study different characteristics of landslide surges by changing water depth and particle density. We find that: (1) the landslide surge propagation process simulation method based on the physics-informed neural network has good applicability, and the stages of landslide surge propagation can be well presented; (2) the depth of water influences the landslide surge propagation as the amplitude of the surge increases with deeper water; (3) the particle density of water influences the landslide surge propagation as the fluctuation of the surge is more obvious with larger particle density. Our study is helpful to understand the propagation process of landslide surges more clearly and provides new ideas for the follow-up study of this kind of complex fluid–structure interaction problem.

Transfer Learning-Based Coupling of Smoothed Finite Element Method and Physics-Informed Neural Network for Solving Elastoplastic Inverse Problems

“…PINNs leverage prior knowledge by integrating observational data and mathematical models to efficiently solve both forward and inverse problems of PDEs. Currently, PINNs have demonstrated remarkable success in various mechanics fields, including material mechanics [38], fluid mechanics [49], fracture mechanics [50], and thermodynamics [51]. Several PINN variants have emerged to address different problems, including conservation PINNs (cPINNs) [52], variational PINNs (vPINNs) [53], fractional-order PINNs (fPINNs) [54], and others.…”

A Deep Learning Method for Dynamic Process Modeling of Real Landslides Based on Fourier Neural Operator

“…Due to the high nonlinearity of the landslide dynamic process itself, there are few studies on applying deep learning methods to large scale landslide simulation (Wu et al., 2022). In our pursuit of ascertaining the applicability of data‐driven deep learning methodologies to large‐scale landslide challenges, we endeavor to extend the utility of FNO to the simulation of landslide dynamic processes.…”