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• Partial Differential Equations
• Numerical Analysis
• Stochastic Differential Equations
• +5 more

Anders Szepessy is a Professor in Mathematics and Numerical Analysis at the Department of Mathematics, KTH Royal Institute of Technology. His research centers on partial differential equations and numerical methods for differential equations, with significant contributions to molecular dynamics, quantum mechanics, and stochastic systems. His primary research domains include: Partial Differential Equations Numerical Analysis Stochastic Differential Equations Molecular Dynamics and Quantum Mechanics Optimal Control and Finite Element Methods Machine Learning Applications Recent publications reveal a pronounced shift toward integrating machine learning with computational physics, particularly in neural network approximations for molecular dynamics and quantum systems. His work on adaptive methods for high-dimensional stochastic and partial differential equations demonstrates consistent innovation in numerical analysis, with applications spanning quantum chemistry, fluid dynamics, and energy systems. Professor Szepessy actively contributes to academic instruction as course responsible and examiner for advanced mathematics courses including Analytical and Numerical Methods for Partial Differential Equations, Computational Methods for Stochastic Differential Equations and Machine Learning, and multiple degree projects in mathematics and scientific computing at KTH.

• Stochastic Control Theory
• Artificial Intelligence for Optimization
• Power Systems Engineering
• +3 more

Magnus Perninge is an Associate Professor in Electrical Engineering at Linnaeus University (LNU), Sweden. He previously completed his undergraduate and PhD studies at KTH Royal Institute of Technology and held postdoctoral/researcher positions at Lund University. His teaching includes courses in Automatic Control (basic and advanced), Electric Power Systems , and Electrical Machines . He supervises Alexander Marcial , a doctoral student in Mathematics. Current Affiliation: Linnaeus University (School of Engineering & Faculty of Technology) Past Affiliations: KTH Royal Institute of Technology, Lund University Research Interests: Magnus specializes in the optimal control of stochastic systems with applications in electrical energy systems . He integrates artificial intelligence and machine learning to solve large-scale optimization problems in power systems, aiming to enhance efficiency and sustainability. His work bridges stochastic analysis , operations research , and renewable energy integration . Research Projects: Efficient Hydropower Planning : Optimizing Sweden's hydropower for future intermittent energy scenarios Sustainable Energy & Food Production : Enhancing rural farm energy systems Smart Electrification : Supporting SMEs in Kronoberg and Kalmar regions Research Groups: AI and Machine Learning for Optimization and Operations Research Stochastic Analysis and Stochastic Processes Waves, Signals and Systems