Advanced Orthogonal Polynomial Methods for Solving Partial Optimal Control Problems

Abstract

This study investigates the application of orthogonal polynomial methods to address optimal partial observation problems in systems governed by partial differential equations (PDEs). It explores computational strategies to resolve challenges associated with incomplete or limited observational data in such systems. The research emphasizes leveraging the unique properties of orthogonal polynomials, such as their inherent orthogonality and spectral convergence, to enhance computational accuracy and stability. By integrating these techniques, the proposed approach aims to reduce numerical errors, accelerate solution convergence, and deliver reliable approximations for optimal observation designs. Theoretical advancements are systematically paired with computational frameworks, enabling rigorous analysis of system dynamics and observability. Numerical experiments demonstrate the practical efficacy of these methods across diverse scenarios, including high-dimensional and nonlinear PDE systems. The findings highlight how orthogonal polynomial-based techniques can transform computational methodologies for optimal observation challenges, offering scalable and precise solutions. This work establishes a robust foundation for advancing real-world applications in control, inverse problems, and data assimilation, positioning orthogonal polynomial methods as critical tools for researchers and practitioners in computational science and engineering.