This course provides an introduction to the tools and techniques in scientific computing for space exploration. Students shall examine qualitative and quantitative methods for the design and analysis of computing algorithms. Specific topics may include observational and experimental studies, fundamental data structures, abstract data types, data mining, integration of satellite imagery with data from other sources, automata theory, filters, SLAM, optimization, and algorithm analysis and various types of algorithms (recursive, backtracking, divide and conquer, dynamic programming, greedy, branch and bound, brute force, and randomized). 

This course examines applications of simulations and algorithms to understand the fundamental processes and phenomena in the Universe. Specific topics may include general theory of systems modelling; principles of discrete-event, continuous, and hybrid system modeling; Monte Carlo methods; evaluation of simulations and observations from telescopes and satellites; autonomous cosmic intelligence; recursive disctinctioning processes; machine learning; measurements of galaxies and structures from galactic and extragalactic surveys; roles of dark matter and dark energy; influences of gravity; N-body simulations; and computing performances for large-scale scientific applications.