Higly Sophisticated Computations
FIT-VNDAcad. year: 2020/2021
The course aims to show practical methods of solving problems encountered in science and engineering. The numerical solution of systems of differential equations is discussed, and parallel cooperation of microprocessors based on differential calculus is evaluated. The course mainly examines VSVO (variable step, variable order) methods. During the course, students are going to use an original method based on a direct calculation of the Taylor series terms. The TKSL (FOS) simulation language is also available, which allows specifying problems directly using equations.
The close correspondence of block and equation representations of differential equations is also analysed, using block representation as input. The course discusses the following technical problems: solution of large systems of differential equations, algebraic equations, partial differential equations, stiff systems, problems in automatic control, electric circuits, VLSI circuits, modelling of mechanical systems, electrostatic and electromagnetic fields. The analysis of the parallel algorithms and design of a specialised architecture for the solution of differential equations is also part of the course. Most of the technical problems lead to the matrix/vector representation. Individual problems are also going to be solved in MATLAB/Simulink.
Areas of interest for SDZ
- Analytical solution of differential equations.
- Numerical solution of differential equations.
- Extremely accurate solution of differential equations using the Taylor series, libraries for accurate calculations.
- Parallel properties of the Taylor series, the basis of the programming of the specialised parallel tasks using differential calculus (close correspondence of block and equation representation).
- The adjunct differential operators and parallel solution of the differential equations with time-variable coefficients.
- The solution of large systems of algebraic equations by converting them to the ordinary differential equations..
- Fourier series, finite integrals.
- Simulation of electric circuits.
- Solution of practical problems described by partial differential equations.
- The concept of a basic specialized processor for parallel computational system.
Learning outcomes of the course unit
Ability to analyse the selected methods for numerical solutions of differential equations (based on the Taylor Series Method) for extremely exact and fast solutions of sophisticated problems.
- An individual solution of a nontrivial system of diferential equations.
Recommended optional programme components
Recommended or required reading
Butcher, J. C.: Numerical Methods for Ordinary Differential Equations, 3rd Edition, Wiley, 2016.
Lecture notes written in PDF format,
Source codes (TKSL, MATLAB, Simulink) of all computer laboratories
Kunovský, J.: Modern Taylor Series Method, habilitation thesis, VUT Brno, 1995
Hairer, E., Norsett, S. P., Wanner, G.: Solving Ordinary Differential Equations I, vol. Nonstiff Problems. Springer-Verlag Berlin Heidelberg, 1987.
Hairer, E., Wanner, G.: Solving Ordinary Differential Equations II, vol. Stiff And Differential-Algebraic Problems. Springer-Verlag Berlin Heidelberg, 1996.
Shampine, L. F.: Numerical Solution of ordinary differential equations, Chapman and Hall/CRC, 1994
Strang, G.: Introduction to applied mathematics, Wellesley-Cambridge Press, 1986
Meurant, G.: Computer Solution of Large Linear System, North Holland, 1999
Saad, Y.: Iterative methods for sparse linear systems, Society for Industrial and Applied Mathematics, 2003
Burden, R. L.: Numerical analysis, Cengage Learning, 2015
LeVeque, R. J.: Finite Difference Methods for Ordinary and Partial Differential Equations: Steady-State and Time-dependent Problems (Classics in Applied Mathematics), 2007
Strikwerda, J. C.: Finite Difference Schemes and Partial Differential Equations, Society for Industrial and Applied Mathematics, 2004
Golub, G. H.: Matrix computations, Hopkins Uni. Press, 2013
Duff, I. S.: Direct Methods for Sparse Matrices (Numerical Mathematics and Scientific Computation), Oxford University Press, 2017
Corliss, G. F.: Automatic differentiation of algorithms, Springer-Verlag New York Inc., 2002
Griewank, A.: Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, Society for Industrial and Applied Mathematics, 2008
Press, W. H.: Numerical recipes : the art of scientific computing, Cambridge University Press, 2007
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Language of instruction
To provide overview and basics of practical use of selected methods for numerical solutions of differential equations (based on the Taylor Series Method) for extremely exact and fast solutions of sophisticated problems.
Specification of controlled education, way of implementation and compensation for absences
During the semester, there will be evaluated computer laboratories. Any laboratory should be replaced in the final weeks of the semester.
Type of course unit
39 hours, optionally
Teacher / Lecturer
- Methodology of sequential and parallel computation (feedback stability of parallel computations)
- Extremely precise solutions of differential equations by the Taylor series method
- Parallel properties of the Taylor series method
- Basic programming of specialised parallel problems by methods using the calculus (close relationship of equation and block description)
- Parallel solutions of ordinary differential equations with constant coefficients
- Adjunct differential operators and parallel solutions of differential equations with variable coefficients
- Methods of solution of large systems of algebraic equations by transforming them into ordinary differential equations
- Parallel applications of the Bairstow method for finding the roots of high-order algebraic equations
- Fourier series and finite integrals
- Simulation of electric circuits
- Solution of practical problems described by partial differential equations
- Library subroutines for precise computations
- Conception of the elementary processor of a specialised parallel computation system.
Guided consultation in combined form of studies
26 hours, optionally
Teacher / Lecturer
Exercise in computer lab
26 hours, compulsory
Teacher / Lecturer
- Simulation system TKSL
- Exponential functions test examples
- First order homogenous differential equation
- Second order homogenous differential equation
- Time function generation
- Arbitrary variable function generation
- Adjoint differential operators
- Systems of linear algebraic equations
- Electronic circuits modeling
- Heat conduction equation
- Wave equation
- Laplace equation
- Control circuits