Numerical solutions of sea turtle population dynamics model by using restarting strategy of PINN-Adam

Abstract

The Lotka–Volterra predator–prey system for dynamic sea turtle population is solved using r-PINN-Adam method, a novel approach which combines Physics-Informed Neural Network (PINN) with restarting strategy. This method allows us to monitor the loss function values of PINN such that when there is no progress made, we stop the process and pick a good value to be used in the next process. Subsequently, the training time decreases and the accuracy increases. All of numerical solutions is compared to the popular Runge–Kutta method and presented graphically. Simulation results also displayed in terms of trainable parameters and optimal loss function performance. The research highlights the robustness and superiority of the proposed method, positioning it as a valuable tool for sea turtle conservation efforts.