analiza aiqbench

This research demonstrates how Simulation-Grounded Neural Networks (SGNNs) significantly enhance predictive accuracy by integrating mechanistic simulations as structural priors within neural architectures. The study validates a robust framework capable of handling incorrect assumptions in training data, offering a scalable solution for scientific forecasting across epidemiology and ecology that bridges the gap between model interpretability and ML power.