Comparative Analysis of Inverse Problem-Solving Techniques: Integrating Deep Learning with Advanced Optimization Methods

This paper presents a comparative analysis of four distinct approaches to solving inverse problems in engineering and scientific applications. The studies under comparison integrate deep learning techniques, specifically using TensorFlow, with various optimization methods: Genetic Algorithm (GA), Ant Colony Optimization (ACO), LMFIT (employing Nelder-Mead and Powell methods), and Particle Swarm Optimization (PSO). All four approaches aim to predict design parameters for achieving desired response profiles. This analysis examines the methodologies, performance metrics, and unique features of each approach, providing insights into their relative strengths and potential applications.