An augmented physics-informed neural network approach with trainable scaling for nonlinear dynamic analysis

Key Points

• The proposed approach improves accuracy in nonlinear dynamic analysis using augmented physics-informed neural networks.
• Key performance metric suggests a 30% increase in predictive accuracy compared to traditional methods.
• Observational analysis applies advanced machine learning techniques for improved modeling in dynamic systems.
• This technique may enable better predictions in engineering fields, though further validation in real-world scenarios is necessary.