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Gavin Lee Goodship

Project Title

ML-Driven Advances in High-Order Numerical Integration Methods: Heuristic Initialization and Optimization of Non-Convex Objective Functions with Equality Constraints.

Project Description

This research project aims to enhance the efficiency and accuracy of numerical integration methods, particularly focusing on the widely used fourth-order Runge-Kutta method. The primary challenges include collecting a diverse dataset of ordinary differential equations (ODEs), optimizing numerical integration computations, and applying machine learning techniques to predict sequential ODE solutions while maintaining required error rates. The project consists of several key objectives:

Research Challenges: Data Collection: Gathering a diverse dataset from various ordinary differential equations (ODEs) to train machine learning models.

Numerical Integration Optimization: Improving the efficiency of numerical integration methods, particularly the fourth-order Runge-Kutta method, while maintaining or enhancing solution accuracy.

Machine Learning Application: Applying machine learning techniques, such as reinforcement learning and transformers, to optimize numerical integration computations, considering the sequential nature of ODE solutions.

Error Control: Ensuring that optimization efforts do not compromise the required error rates of the chosen numerical algorithms.

Research Objectives: Data Collection and Preparation: Collecting a comprehensive dataset of ODEs, including starting coefficients and approximation endpoints.

Numerical Integration Optimization: Developing heuristic initialization techniques to reduce computational costs while preserving solution accuracy.

Machine Learning Application: Adapting machine learning techniques to predict sequential ODE solutions and enhance computational efficiency. Error Control and Validation: Rigorously validating optimized methods to ensure they meet required error rates.

Overall Impact: Contributing to the field of numerical analysis by advancing numerical integration methods, making them more efficient and reliable.