Dr. Konrad Simon

Some more infos can be found on my new (temporay) homepage.

Research Interests

• Multiscale finite elements
• Partial differential equations
• Geophysical fluid dynamics
• Large scale data-driven simulations
• High-performance computing and (parallel) visualization
• Scientific software development with C++, Python and Julia, see also my homepage for more on this
• Deep neural nets

About me

I started releasing codes for research and teaching on my github page.

I am an applied mathematician working in the field of scientific computing. My current main objective is the simulation and design of numerical models and software for application processes that involve a wide range of relevant scales. Problems I am aiming to solve mostly come from geosciences but also comprise applications in computer science, engineering, physics or other fields of science. This involves in particular experience with models based on differential equations and numerical methods such as finite elements (FEM) as well as data driven algorithms, optimization and their large scale implementation using mostly C++ and other tools from software development. On this path I am involved in training and supervision of BSc/MSc/PhD students and scientific outreach activities.

It is my goal to bring scientific innovations further into real world applications. Recently, I also got interested in the application of neural nets to improve simulations and in agile project management which I see as a huge step into the right direction to trigger authentic creative potential in teams.

Peer-reviewed Journal and Conference Publications

1. C. Eldred, J. Behrens, K. Simon, “Stable 3D Mixed Multiscale Finite Elements for the L2-de Rham Complex with Rough Coeffcients“, in preparation, source code
2. Y. Chen, J. Behrens, K. Simon, “Deep Neural Nets for Upscaling Processes in Multiscale Simulations with High Contrast“, in preparation
3. Y. Chen, K. Simon, J. Behrens, “Extending Legacy Climate Models by Adaptive Mesh Refinement for Single Component Tracer Transport“, submitted (2019), Preprint
4. K. Simon., J. Behrens, Semi-Lagrangian Subgrid Reconstruction for Advection-Dominant Multiscale Problems, submitted (2019), source code, Preprint
5. K. Simon., J. Behrens, A Semi-Lagrangian Multiscale Framework for Advection-Dominant Problems, Proceedings of the International Conference on Computational Science (ICCS) 2019, Faro, Portugal (2019), PDF
6. Y. Chen, K. Simon., J. Behrens, Enabling Adaptive Mesh Refinement for Single Components in ECHAM6, Proceedings of the International Conference on Computational Science (ICCS) 2018, Wuxi, China (2018), PDF
7. K. Simon., J. Behrens, Multiscale finite elements for transient advection-diffusion equations through advection-induced coordinates, SIAM Multiscale Modeling and Simulation (MMS), accepted (2018), Preprint
8. K. Simon., R. Basri, Elasticity-based Matching by Minimizing the Symmetric Difference of Shapes, IET Computer Vision (2017), PDF
9. K. Simon., S. Sheorey, D. Jacobs, R. Basri, A Hyperelastic Two-Scale Optimization Model for Shape Matching, SIAM Journal on Scientific Computing SISC (2016), PDF
10. K. Simon, S. Sheorey, D. Jacobs, R. Basri, A Linear Elastic Force Optimization Model for Shape Matching, Journal of Mathematical Imaging and Vision (2015), PDF
11. Y. Guo, K. Simon and E.S. Titi, Global Well-posedness of a System of Nonlinearly Coupled KdV equations of Majda and Biello, Communications in Mathematical Sciences (2014), PDF

Thesis

1. K. Simon, Linear and Nonlinear Elasticity Models for Shape Matching, PhD thesis, The Weizmann Institute of Science (Israel), December 2015, PDF
2. K. Simon, Methoden der Theorie Monotoner Operatoren und ihre Anwendung auf Integral- und Integro-Differenzialgleichungen, Diploma thesis (in German), University of Leipzig (Germany), 2009, PDF