T. Lazovskaya

Embedding Physics-Informed Neural Networks into Numerical Schemes for Modeling Dynamical Systems

Context and relevance. Physics-Informed Neural Networks (PINNs) are considered a promising tool for mathematical modeling of dynamical systems described by differential equations. However, classical PINN approaches require repeated computation of high-order derivatives, which leads to significant computational costs an...