Physics-informed neural networks for bulge test modeling of general anisotropic two-dimensional crystalline materials with decoupled elasticity

Key Points

• Effective modeling of general anisotropic two-dimensional crystalline materials shows improved elasticity predictions.
• Key evidence indicates that physics-informed neural networks outperform traditional methods in analyzing bulge tests.
• Analysis utilizing physics-informed neural networks provides insights into the complexities of decoupled elasticity.
• This work supports the advancement of simulation techniques in engineering applications, pushing boundaries in material science.