Work

Physics-Informed Neural Network for Solving Forward and Inverse Problems of Granular Flow in the Homogeneous Cooling State

Year: 2025

Type: article

Abstract: The advent of machine learning has prompted the emergence of innovative methodologies for predicting the hydrodynamics of granular flows. In this study, the physics-informed neural network (PINN) approach was employed to solve the forward and inverse problems of a simple granular flow with smooth particles in the homogeneous cooling state. The three techniques, which are the dimensionless granular temperature as the optimization of the loss functions, the normalized time information as the input layer, and adjusting local weights of sample points based on the physical characteristics, have been shown to significantly contribute to enhancing the precision of classical PINN in predicting the variation of granular temperature over time. The proposed method (developed PINN) has been validated for many different cases and the influence of numbers of sampled date in the solution process was also investigated.

Source: Smart Chemical Engineering

DOI: 10.53941/sce.2025.100006

Authors: Bing Wan, Bidan Zhao, Lijing Mu, Junwu Wang

Institutions: China University of Petroleum, Beijing, State Key Laboratory of Heavy Oil, China Special Equipment Inspection and Research Institute, State Administration for Market Regulation

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Natural Science Foundation of China, China University of Petroleum, Beijing

Awards: G6259247475, G7072847438, G946235598