<ul data-eligibleForWebStory="true"><li>Extended Stability Runge-Kutta (ESRK) methods are essential for large-scale computational problems in various fields but optimizing them for efficiency and low storage is challenging.</li><li>A hybrid Genetic Algorithm (GA) and Reinforcement Learning (RL) approach is proposed to automate heuristic discovery for optimizing low-storage ESRK methods.</li><li>The new approach combines GA-driven mutations for search-space exploration and an RL-inspired state transition mechanism for heuristic selection, leading to a 25% reduction in runtime while maintaining stability and accuracy.</li><li>The study validates the proposed heuristic optimisation framework on benchmark problems, showcasing its potential to improve resource efficiency in high-fidelity simulations using low-storage Runge-Kutta methods.</li></ul>

Optimising 4th-Order Runge-Kutta Methods: A Dynamic Heuristic Approach for Efficiency and Low Storage

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