Jannick Wolters

Applied

Mathematician

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Jannick Wolters

About me.

My passion is solving complex real world problems from the realm of transport equations on modern HPC architectures.
Most recently, my interests focus on useful applications of machine learning and GPGPU acceleration techniques.
Being a quick learner and reliable team player, I have been working on a wide range of successful projects with fellow Ph.D. students as well as industry partners.
As I am now close to finishing my Ph.D. at Karlsruhe Institute of Technology in late Oktober, I am already looking forward to new and exiting challenges.

Experience.

Education.

ongoing

Ph.D. in Mathematics

Karlsruhe Institute of Technology (KIT)

Steinbuch Centre for Computing (SCC), CSMM Research group

Supervisor: Prof. Dr. Martin Frank

Thesis: "Uncertainty Quantification for the evaluation of PGNAA spectra"

03/2017 - 05/2021

Research Scientist

Karlsruhe Institute of Technology (KIT)

Steinbuch Centre for Computing (SCC), CSMM Research group

04/2015 - 03/2017

Master of Computational Engineering Science

RWTH Aachen University

Supervisor: Prof. Dr. Martin Frank

Thesis: "Uncertainty Quantification for Wind Farm Models"

03/2017 - 05/2021

Student research / teaching assistant

RWTH Aachen University

Research: "Fully coupled MHD-simulations in OpenFOAM"

Teaching: "Partial differential equations"

10/2013 - 03/2014

Research Internship

ABB Research Switzerland

Subject: "Power Device Simulations in OpenFOAM"

10/2010 - 03/2015

Bachelor of Computational Engineering Science

RWTH Aachen University

Supervisor: Prof. Dr. Manuel Torrilhon

Thesis: "MHD Simulations in OpenFOAM"

Skills.

Mathematics

Computer Science

Software Packages

Topics

  • Particle transport
    PRO
  • Uncertainty Quantification
    PRO
  • Inverse Problems
    ADV
  • Machine learning
    BASIC

Numerical Methods

  • Finite Volume Method
    PRO
  • Finite Element Method
    PRO
  • Sparse Reconstruction
    ADV
  • Continous Optimization
    BASIC

Equations

  • Boltzmann
    PRO
  • Navier-Stokes
    PRO
  • Magnetohydrodynamics
    ADV
  • Maxwell
    BASIC

HPC

  • MPI
    PRO
  • OpenMP
    PRO
  • OpenACC
    BASIC
  • PETSc
    BASIC

Data Science

  • numpy
    PRO
  • scipy
    PRO
  • pandas
    PRO
  • Tensorflow
    ADV
  • Keras
    ADV

Software development

  • Git
    PRO
  • Docker
    PRO
  • CI / CD
    PRO
  • CMake
    PRO
  • Scrum
    ADV

Languages

C++
PRO
Python
PRO
Matlab
PRO
Julia
ADV
Fortran
ADV
R
BASIC

Fluid Dynamics

Particle transport

IDE's

  • Matlab
    PRO
  • QtCreator
    PRO
  • ParaView
    ADV
  • VSCode
    ADV

Projects.

ZEBRA

EFRE.NRW Project

As a winner of the market competition EnergieUmweltwirtschaft.NRW, the federal state NRW funded innovative developments in the field of environmental technologies with the help of European Union funds. In cooperation with AiNT GmbH an innovative measurement system was developed for non-destructive environmental and hazardous substance analysis. This measurement system is based on the prompt and delayed gamma neutron activation analysis (P&DGNAA). The project included the development of new analytical methods for determining the mass fractions of materials inside an unknown sample.

Finite element method, Particle transport, Inverse problems, Uncertainty quantification, Sparse reconstruction

Python, C++, MPI, OpenMP, Docker, gRPC

UQCreator

HPC Uncertainty Quantification framework

The uncertainty quantification framework UQCreator provides various uncertainty propagation tools for hyperbolic conservation laws. Its main idea is to use kinetic numerical fluxes and source terms, which evaluate a given deterministic problem at different sample points. This hybridization of intrusive and non-intrusive methods significantly reduces numerical costs, yields an efficient parallelization with OpenMP and MPI while allowing an easy extension to various problems into the framework.

Uncertainty quantification, Finite volume method

C++, MPI, OpenMP

KiT-RT

HPC Radiation Therapy framework

The KiT-RT (Kinetic Transport Solver for Radiation Therapy) framework is a high-performance open source platform for radiation transport. Its main focus is on radiotherapy planning in cancer treatment. Furthermore, it also provides tools to investigate various research questions in the field of radiative transfer. This goal is supported by an easily extendable code structure that allows for straightforward implementation of additional methods and techniques.

Particle transport, Finite volume method

C++, Python, MPI, OpenMP

Renewable Energy

Sensitivity Analysis for Offshore Wind Farms

As uncertainties in modeling/input parameters like wind direction, turbine energy generation characteristics and economic factors are crucial in the prediction of the annual power production of an offshore wind farm and generally very difficult to assess, we developed a powerful tool to quantify the effects of these uncertainties for arbitrary wind farms. The tools makes use of advanced techniques for high dimensional numerical integration and therefore is capable of computing comprehensive sensitivity information with low computational effort.

Renewable energy, Uncertainty quantification

C++, OpenMP

Publications.

"Sparse signal reconstruction for prompt gamma neutron activation analysis."
J. Wolters, K. Krycki, M. Frank
in progress
"Uncertainty Quantification of Offshore Wind Farms Using Monte Carlo and Sparse Grids."
P. Richter, J. Wolters, M. Frank
Journal of Energy Sources, Part B: Economics, Planning, and Policy, in revision
"Entropy–based methods for uncertainty quantification of hyperbolic conservation laws."
M. Frank, J.Kusch, J. Wolters
Springer International Publishing, 2021