The Computational Mathematics Research Group within the Department of Mathematics at FMIPA, UGM (Universitas Gadjah Mada) focuses on the intersection of mathematical sciences, algorithms, and high-performance computation. They develop numerical methods and computational models to solve applied scientific and potentially real-world industrial problems. The group’s office is located on the 5rd floor of the D building, Faculty of Mathematics and Natural Science (FMIPA) at the Sekip Utara campus in Yogyakarta.
Core Focus Areas:
- Computational Inverse Problems: Includes tomographic reconstruction (e.g., X-ray) and data-driven computational inversion.
- Computational Differential Equations: Developing numerical methods to solve partial differential equations (PDEs).
- Optimization & Numerical Methods: Resolving fundamental scientific problems using computer algebra systems (like Python, C++, and MATLAB).
- Machine learning and Deep learning: Developing machine learning, deep learning, and artificial neural networks to solve any problems including problems in partial differential equartions (PDEs).
Key Faculty & Researchers:
- Prof. Imam Solekhudin, M.Si., Ph.D.: Specializes in boundary integral equation methods, infiltration from channels, heat transfer in non-layered and layered materials, pollutant spread in water, and diffusion-convection problems.
- Dr. Sumardi, M.Si.: Focuses on numerical methods, computational differential equations, and mathematical models for shallow water equations.
- Zenith Purisha, S.Si., M.Sc., Ph.D.: Specializes in computational inverse problems and tomographic reconstruction.
- Ari Dwi Hartanto, S.Si., M.Sc., Ph.D.: Expertise lies in computer algebra, Gröbner bases, and numerical computation.
- Rudi Adha Prihandoko, S.Si., M.Sc.: Expertise lies in computational fluid dynamics, finite volume methods, and shallow water equations.
- Ivan L. I., Ph.D.: Specializes in high-performance computing, machine learning and numerical algorithm for PDEs problems, multiscale finite element methods.
Academic Offerings & Events:
For undergraduate and graduate students in the Mathematics Undergraduate Program, the group offers various elective courses and supervised topics. Examples of these include Introduction to Numerical Analysis I & II, Introduction to Fractal Geometry, Finite Element Methods, Image Processing, Computational Machine Learning, and Capita Selecta in Computational Mathematics, etc. The group also frequently hosts collaborative hybrid events, such as the Computational Mathematics Colloquia, allowing students and visiting international experts to discuss topics ranging from high-performance computing methods to machine learning applications.