CN101 Computational Science Course Syllabus PDF

CN101 COMPUTATIONAL SCIENCE 1 Course Syllabus 1st PER / PRELIM 2nd PER / MIDTERM 3rd PER / PREFINAL 4th PER / FINAL Overview of Computational Programming Data Analysis: Techniques...

CN101 COMPUTATIONAL SCIENCE 1 Course Syllabus 1st PER / PRELIM 2nd PER / MIDTERM 3rd PER / PREFINAL 4th PER / FINAL Overview of Computational Programming Data Analysis: Techniques: Science * Introduction to Python * Data Collection and * Performance Optimization * History and Evolution * Data Structures & Preprocessing Computational Methods Algorithms * Statistical Analysis * Numerical Methods for ODEs and PDEs * Applications & * Scientific Libraries Visualization: Other Methods: Importance (NumPy, SciPy) * Data Visualization Tools * Monte Carlo Methods Mathematical Foundations Modeling: (Matplotlib, Seaborn) * Finite Element Analysis * Linear Algebra * Introduction to Modeling High Performance * Discrete and Continuous Computing Models * Parallel Computing Concepts * Differential Equations Simulation: * Performance Optimization Case Studies and * Probability and Statistics * Simulation Technique Techniques Applications 2 Grading System Class Standing (40%) Quizzes 20% Recitation/Report/Presentation 10% Project/Case Study/Term Paper/ Reaction Paper Assignment/Seatwork 10% Minor Exam (25%) Written Major Exam (35%) Practical/Written 3  Overview Computational science is a rapidly growing interdisciplinary field. There are many problems in science and technology that cannot be sufficiently studied experimentally or theoretically. It may be too expensive or too dangerous, or simply impossible due to the space and timescales involved. In fact, computational science is considered by many to be a third methodology in scientific research, along with theory and experiment, and working in tandem with them. Computational science can be used to corroborate theories that cannot be confirmed or denied experimentally, for example theories relating to the creation of the universe. On the other hand, advances in experimental techniques and the resulting data explosion, allow for data-driven modelling and simulation. 4 Below are the following research categorizations: Algorithms (numerical and non-numerical): mathematical models, computational models, and computer simulations developed to solve sciences (physical, biological, social), engineering, and humanities problems Computer hardware that develops and optimizes the advanced system hardware, firmware, networking, and data management components needed to solve computationally demanding problems The computing infrastructure that supports both the science and engineering problem solving and the developmental computer and information science 5  Concepts Computational Science is a branch of study that deals with using computing systems to apply mathematical models, with the purpose of describing and solving natural systems and ultimately finding answers to scientific problems. Computational science, also known as scientific computing, technical computing or scientific computation (SC), is a division of science, and more specifically the Computer Sciences, which uses advanced computing capabilities to understand and solve complex physical problems. Mathematical models have the capability to describe any natural system. With computers, understanding, processing, and simulating these behaviors using mathematical models become easier than by human capability alone. The increasing and ever-advancing capabilities of today’s computers are what makes computational science possible and practical. Computation is a relatively new addition to the long-existing other two branches of science. Any behavior, process, or system, can be described and studied by Theory, Experimentation, and Computation. They feed into each other and validate each other, ultimately forming a complete 6 understanding of any system. Computational science can also be used where it becomes impossible or impractical to apply experimental methods. Computational science is often thought of as an integration of three disciplines- Mathematics, Computer Science, and Applied Science. 6 Computational Scientific Approach Theory Model Analysis/Simulation Assessment The computational scientific approach to a problem has a general series of steps- Theory: A scientific theory, or the problem statement, is used as the basis of a model. Model: A mathematical model of the system is devised. Algorithms can be formulated to perform analyses based on the model. The rules that drive a simulation are set. Analysis/ Simulation: Numerical analysis or simulations can be run based on 7 algorithms. The procedure is implemented using programs and toolsets with the required capabilities. Assessment: The data from simulation results, as well as experimental results in certain cases, are used for refining further iterations. The process is repeated until the model is solved, or a conclusion reached. 7  Ways to study a system 1. Experiment with the actual system 2. Experiment with a model of the system 8  COMPUTATIONAL SCIENCE History and Evolution 9 Charles Babbage 10  Computer Science: The Evolution The computer science field began in the 1830s with Ada Lovelace, recognized as the first computer programmer. In the 1940s, Alan Turing began the first experiments with artificial intelligence In the 1950s, Grace Hopper created what would become the first programming language Begin in 1950s: Von Neumann's work leading to - ENIAC > MANIAC - ORACLE and their use in solving engineering problems Clearly SC/CSE but not recognized as such until 1982 Lax Report 11 Computational Science is a discipline of its own: CS – Computer Science MTH – Applied Mathematics SE – Science and Engineering CSE – Computational Science & Engineering 12 Evolution of Computational Science Computational needs continuously growing Research communities and projects have become global The need to share data and resources 13 14 Assignment – Study about the following: Applications & Importance Mathematical Foundations NEXT MEETING…. 15