Allan P. Engsig-Karup
Associate Professor in Scientific Computing, M.Sc.(Eng.), Ph.D.
Faculty Member of Center For Energy Resources Engineering (CERE), DTU

	  P. Engsig-Karup Scientific Computing
DTU Compute, Technical University of Denmark
Matematiktorvet, Building 303b/108
2800 Kgs.-Lyngby
Tel: (+45) 45 25 30 73
Fax: (+45) 45 88 26 73
Skype: a.p.engsigkarup
apek @

Scientific Computing
DTU Compute
Anne Mette Eltzholtz Larsen
Building 303B, room 105
Tal: (+45) 45 25 52 46

Research interests linked to applications of basic and applied mathematics research for Advanced Simulations and innovative use of Modern Computing Technologies

Publications, presentations, etc. or check out my profile at ResearchGate.

Internships with companies and research institutions

University Teaching

Course catalogue at DTU: Catalogue

Organized PhD schools, Workshops and Seminars

At the Scientific Computing Section of DTU Compute, we also host Scientific Computing Seminars.

Research and Innovation projects

Active and recent research areas in new technologies, computational mathematics and their applications

All highlights are build from scratch with an aim to deliver state-of-the-art approaches using advanced numerical methods as a part of research effort in my group.

Research in paradigm shifts in scientific computing using modern parallel programming paradigms and emerging many-core and heterogeneous architectures ranging from work desktops to the largest super-computing clusters in the world.
Novel algorithms and high-performance computing for fast (possibly real-time) calculations to pave the way for novel marine and naval hydrodynamics calculations.
Novel and efficient spectral (high-order) algorithms for uncertainty quantification of problems with high dimensionality, data science, machine learning, stochastic simulations.
Massively parallel and scalable algorithms such as multigrid and multi-level algorithms for scientific engineering applications, e.g. reservoir simulation and hydrodynamics.

Novel application proof-of-concepts for engineering analysis.
Research in efficient and robust high-order unstructured numerical methods and time-dependent models.

Scientific Software Engineering

Drivers of the research are scientific software for scientific investigation.

Some work are described in chapters of Designing Scientific Applications on GPUs.

Poster highlights

Supervision in Research projects (selected and recent)

  • High-Performance Computing (PhD Project, Apr 2018 - May 2018)
  • Data-driven solutions of nonlinear partial differential equations through physics-informed neural networks (B.Sc., Feb 2018 - Jun 2018)
  • Reduced Basis Methods for Parametrized Partial Differential Equations (PhD Project, Dec 2017 - Jan 2018)
  • Application of the Spectral Element Method for an engineering hydrodynamic application (project, Jan 2018 - Jan 2018)
  • Boundary conditions in wave-based room acoustic simulation methods (M.Sc., Jan 2018 - Jun 2018)
  • Advanced Numerical Methods for Differential Equations (project, Oct 2017 - Jan 2018)
  • Applied Machine Learning for Prediction (Project, Sep 2017 - Dec 2017)
  • Spectral Element Modelling of Wave-Body Interactions (M.Sc., Aug 2017 - Feb 2018)
  • Function approximation using Artificial Neural Networks (B.Sc., Feb 2017 - Jun 2017)
  • Robust Spectral Element Methods for Free Surface flows with Structures (M.Sc., Mar 2016 - Sep 2016)
  • Numerical methods for solving Navier-Stokes equations (M.Sc., Mar 2016 - Aug 2016)
  • Multi-scale Finite Volume Method for accelerating reservoir simulations (M.Sc., Sep 2015 - Feb 2016)
  • Multi-level algorithms for uncertainty quantification (M.Sc., Sep 2015 - Feb 2016)
  • Massively Parallel Nonlinear Multigrid on Modern Architectures (M.Sc., Mar 2015 - Sep 2015)
  • Robust massively parallel free surface simulation using the Spectral Element Method (M.Sc., Feb 2015 - Aug 2015)
  • Mathematical Techniques for Reduced Order Modelling (M.Sc., Nov 2014 - April 2015)
  • Spectral element modelling of wave-floating body interactions (M.Sc., Mar 2015 - Sep 2015)
  • Free Surface Hydrodynamics using Isogeometric Analysis (B.Sc., Feb 2015 - Jun 2015)
  • Scientific software solution for visualization of large data sets (Minor project, Feb 2015 - Jun 2015)
  • Hybrid Coupling of Modern Finite Element Methods (M.Sc., Nov 2014 - April 2015)
  • Mesh Adaptive Techniques for Finite Element Solvers (B.Sc., Sep 2014 - Jan 2015)
  • Financial Modelling using PDEs (Project, Aug 2014 - Dec 2014)
  • Software design for portable and scalable scientific calculations on modern and emerging heterogeneous many-core architectures (M.Sc., Jan 2014 - Nov 2014)
  • Implementing exact methods for discrete optimization on a GPU (M.Sc., Feb 2014 - Jun 2014)
  • Implementing metaheuristics using GPU programming (M.Sc., Feb 2014 - Jun 2014)
  • Advanced Techniques for Investigating Structures in Computational Fluid Dynamics (M.Sc., Dec 2012 - May 2013)
  • Spectral Methods for Uncertainty Quantification (M.Sc., Dec 2012 - May 2013)
  • Study in Modern Uncertainty Quantification Methods (M.Sc., Dec 2012 - May 2013)
  • Nonlinear Multigrid for Efficient Reservoir Simulation (M.Sc., Mar 2012 - Sep 2012)
    Project was awarded the DANSIS Graduate Prize 2013.
  • Feasibility study of the parareal algorithm (M.Sc., Feb 2012 - Aug 2012)
    Award for best presentation at Facing the Multicore Challenge III in 2012.
  • GPU-Acceleration of Linear Algebra using OpenCL (B.Sc., Feb 2012 - Jun 2012)
  • Development of a GPU-accelerated MIKE 21 Solver for Water Wave Dynamics (B.Sc., Feb 2012 - Jun 2012)
  • Curving Dynamics in High Speed Trains (M.Sc., Feb 2011-Aug 2011)
  • Fusion Plasma Thermal Transport Radial and Poloidal Profile Modeling (M.Sc., Sep 2010-Jun 2011)
  • Acceleration of a non-linear water wave model using a GPU (M.Sc., May 2010-Dec 2011)
  • Marine simulation and rendering (M.Sc., Sep 2009 - Mar 2010)
  • Simulation of launch and landing of small rocket (Minor project, Feb 2009-Jun 2009)

Community service

2010, Co-founder (with colleagues at Scientific Computing Section) of GPULAB with strong focus on Scientific GPU Computing, fundamental aspects of high-performance and heterogenous computing on modern many-core architectures and software development for proofs of concepts related to next-generation scientific applications using modern programming paradigms (CUDA/OpenCL) and tools. GPUlab was designated Nvidia CUDA Teaching Center May 2012 and Nvidia CUDA Research Center (PI: Allan P. Engsig-Karup) since November 2012.

I am a member of the Scientific Council of Danish Center for Applied Mathematics and Mechanics (DCAMM).