Benchmarking solution methods for parameter identification in dynamical systems

Abstract Accurate models are crucial for simulating, optimizing, and controlling real‐world processes. Parameter identification—the task of estimating the unknown parameters a dynamical system based on measurements—is challenging there exist various methods to approach it. Integration‐based methods, such as shooting full discretization, approximate model output by numerically solving system. Gradient matching other hand, avoid focus minimizing error between measurement slope state derivatives instead. All approaches have advantages disadvantages recipe which method is best in particular situation does not exist. In this paper, we present results benchmark comparing single shooting, multiple gradient using comprehensive database test problems. We investigate if notable difference performance one superior others. From benchmark, conclude that integration‐based outperform matching. While discretization exhibits robustness, provide higher precision. Additionally, observe finding an optimal configuration across diverse set parameter identification problems remains often requires fine‐tuning.