Course detail

Numerical Computations with Partial Differential Equations

FEKT-DPA-TE2Acad. year: 2024/2025

The content of the seminar consists of two related units. The first part deals with the numerical solution of the partial differential equations (PDE), exploiting the Finite Difference method (FDM) and the Finite Element Method. The following PDE are solved by these methods: Laplace’s, Poisson’s, Helmholtz’s, parabolic, and hyperbolic one. The boundary and initial condition as well as the material parameters and source distribution is supposed to be known (forward problem). The connections between the field quantities and the connected circuits as well as the coupled problems are discussed to the end of this part.
The above mentioned FDM and FEM solutions are applied in the second part of the seminar to the evaluation of material parameters of the PDE’s implementing them as a part of the loop of different iterative processes. As the initial values are chosen either some measured data or starting data. The numerical methods utilizing PDE are used for the solution of the optimization problems (finding optimal dimensions or materiel characteristics) and inverse problems (different variants of a tomography known as the Electrical Impedance Tomography, the NMR tomography, the Ultrasound tomography), material models macro, micro and nanoscopic; photonics, nanoelectronics, biophotonics, plasma etc. Each topic is illustrated by practical examples in the ANSYS and MATLAB environment.

Language of instruction


Number of ECTS credits


Mode of study

Not applicable.

Entry knowledge

Mathematical calculus, Physics, Electromagnetism on the level of MSc.

Rules for evaluation and completion of the course

Total number of points 100.
The content and forms of instruction in the evaluated course are specified by the lecturer responsible for the course.


To understand the fundamentals of the PDR numerical solution for application in electrical engineering.
Get acquainted with new applications using MKP and MKD in optimization and inverse tasks.
To acquire theoretical knowledge as well as practical application of the FEM and FDM together with the ability to program corresponding forward and inverse problems.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

J.A.Stratton, Electromagnetic Theory, McGraw-Hill Book Company, New York and London, 1941, (EN)
Sadiku, M.: Electromagnetics (second edition), CRC Press, 2001 (EN)

Recommended reading

SIAM Journal on Control and Optimization, ročník 2013 a výše (EN)
IEEE Transactions on Magnetics, ročník 2012 a výše (EN)

Classification of course in study plans

  • Programme DPA-KAM Doctoral, any year of study, summer semester, compulsory-optional
  • Programme DPAD-EEC Doctoral, any year of study, summer semester, compulsory-optional
  • Programme DPA-EKT Doctoral, any year of study, summer semester, compulsory-optional
  • Programme DPA-MET Doctoral, any year of study, summer semester, compulsory-optional
  • Programme DPA-SEE Doctoral, any year of study, summer semester, compulsory-optional
  • Programme DPA-TLI Doctoral, any year of study, summer semester, compulsory-optional
  • Programme DPA-TEE Doctoral, any year of study, summer semester, compulsory

Type of course unit



39 hours, optionally

Teacher / Lecturer


Introduction to the functional analysis, differential operators, survey of the partial differential equations. Boundary and initial conditions. Finite difference methods (FDM).
Finite element methods (FEM). – introduction. Discretization of a region into the finite elements. Approximation of the field from the nodal or edge values.
Forward problem. Setup of equations for nodal and edge values by the Galerkin method.
Application of the Galerkin method to the static and quasistatic fields (Poisson’s and Helmholtz’s equation).
Application of FEM and FDM on the time variable problems (the diffusion and wave equation).
Connection of the field region with the lumped parameter circuit. Coupled problems.
The field optimization problem. Survey of the deterministic methods. The local and global minima.
Unconstrained problems – gradient method, method of the steepest descent, Newton’s methods.
Constrained optimization problems together with FEM.
Inverse problems for the elliptic equations. The Least Square method. Deterministic regularization methods.
A survey on level set methods for inverse problems and optimal design.
A survey on inverse problems in tomography.
A note: Practical examples using the ANSYS and MATLAB environment will be a part of each point of the curriculum.