Physics-Constrained Graph Neural Networks for Spatio-Temporal Prediction of Drop Impact on OLED Display Panels

TL;DR

This paper introduces a physics-constrained graph neural network that improves spatio-temporal prediction accuracy and stability for modeling drop impacts on OLED display panels, enabling real-time design optimization.

Contribution

The study develops a physics-constrained MeshGraphNet that reduces non-physical artifacts and enhances robustness, with novel noise injection and relative change prediction strategies.

Findings

Improved prediction stability using relative change modeling.

Enhanced model robustness through targeted noise injection.

Superior performance in OLED panel design optimization.

Abstract

This study aims to predict the spatio-temporal evolution of physical quantities observed in multi-layered display panels subjected to the drop impact of a ball. To model these complex interactions, graph neural networks have emerged as promising tools, effectively representing objects and their relationships as graph structures. In particular, MeshGraphNets (MGNs) excel in capturing dynamics in dynamic physics simulations using irregular mesh data. However, conventional MGNs often suffer from non-physical artifacts, such as the penetration of overlapping objects. To resolve this, we propose a physics-constrained MGN that mitigates these penetration issues while maintaining high level of accuracy in temporal predictions. Furthermore, to enhance the model's robustness, we explore noise injection strategies with varying magnitudes and different combinations of targeted components, such as the ball, the plate, or both. In addition, our analysis on model stability in spatio-temporal predictions reveals that during the inference, deriving next time-step node positions by predicting relative changes (e.g., displacement or velocity) between the current and future states yields superior accuracy compared to direct absolute position predictions. This approach consistently shows greater stability and reliability in determining subsequent node positions across various scenarios. Building on this validated model, we evaluate its generalization performance by examining its ability to extrapolate with respect to design variables. Furthermore, the physics-constrained MGN serves as a near real-time emulator for the design optimization of multi-layered OLED display panels, where thickness variables are optimized to minimize stress in the light-emitting materials. It outperforms conventional MGN in optimization tasks, demonstrating its effectiveness for practical design applications.