D. Pflüger

发表

David Holzmüller, D. Pflüger, 2019 .

Demonstrating Performance Portability of A Custom OpenCL Data Mining Application to the Intel(R) Xeon Phi(TM) Coprocessor

Michael Klemm, Dirk Pflüger, Ayal Zaks, 2013 .

Alternating Direction Method of Multipliers for Hierarchical Basis Approximators

Dirk Pflüger, Valeriy Khakhutskyy, D. Pflüger, 2014 .

Solving High-Dimensional Dynamic Portfolio Choice Models with Hierarchical B-Splines on Sparse Grids

D. Pflüger, Peter Schober, J. Valentin, 2019, Computational Economics.

Required Tools in Short

H. Bungartz, D. Pflüger, S. Zimmer, 2014 .

Workshop : Nonlinear and Adaptive Approximation in High Dimensions

S. Bartels, R. Pabel, D. Pflüger, 2007 .

Intelligent Methods for Test and Reliability

H. Amrouch, D. Weiskopf, T. Ertl, 2022, 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE).

Machine Learning for Test, Diagnosis, Post-Silicon Validation and Yield Optimization

K. Chakrabarty, H. Amrouch, M. Reorda, 2022, 2022 IEEE European Test Symposium (ETS).

Efficient and scalable distributed-memory hierarchization algorithms for the sparse grid combination technique

Dirk Pflüger, Mario Heene, D. Pflüger, 2015, International Conference on Parallel Computing.

A mass-conserving sparse grid combination technique with biorthogonal hierarchical basis functions for kinetic simulations

K. Kormann, D. Pflüger, Theresa Pollinger, 2022, J. Comput. Phys..

PDEBENCH: An Extensive Benchmark for Scientific Machine Learning

Mathias Niepert, F. Alesiani, D. Pflüger, 2022, Neural Information Processing Systems.

From Task-Based GPU Work Aggregation to Stellar Mergers: Turning Fine-Grained CPU Tasks into Portable GPU Kernels

Dominic C. Marcello, D. Pflüger, P. Diehl, 2022, ArXiv.

PDEBENCH: An Extensive Benchmark for Scientific Machine Learning

Mathias Niepert, F. Alesiani, D. Pflüger, 2022, ArXiv.

Solving High-Dimensional Dynamic Portfolio Choice Models with Hierarchical B-Splines on Sparse Grids

D. Pflüger, Peter Schober, J. Valentin, 2021, Computational Economics.

Enabling unstructured domain decompositions for inhomogeneous short-range molecular dynamics in ESPResSo

C. W. Glass, D. Pflüger, S. Hirschmann, 2019, The European Physical Journal Special Topics.

Learning Free-Surface Flow with Physics-Informed Neural Networks

Dirk Pflüger, Raphael Leiteritz, Marcel Hurler, 2021, 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA).

Spatially adaptive sparse grids for high-dimensional data-driven problems

Benjamin Peherstorfer, Dirk Pflüger, Hans-Joachim Bungartz, 2010, J. Complex..

Spatially Adaptive Sparse Grids for High-Dimensional Problems

Dirk Pflüger, D. Pflüger, 2010 .

Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario

Dirk Pflüger, Bernard Haasdonk, Andrea Barth, 2018, Computational Geosciences.

PDEBENCH: An Extensive Benchmark for Scientific Machine Learning

Mathias Niepert, F. Alesiani, D. Pflüger, 2022, ArXiv.

AutoTuneTMP: Auto-Tuning in C++ With Runtime Template Metaprogramming

Dirk Pflüger, Malte Brunn, David Pfander, 2018, 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).

Hierarchical Gradient-Based Optimization with B-Splines on Sparse Grids

Dirk Pflüger, Julian Valentin, D. Pflüger, 2016 .

Gradient-Based Two-Scale Topology Optimization With B-Splines on Sparse Grids

Dirk Pflüger, Julian Valentin, Daniel Hübner, 2020, SIAM J. Sci. Comput..

Performance-Portable Distributed k-Nearest Neighbors using Locality-Sensitive Hashing and SYCL

Dirk Pflüger, Gregor Daiß, Marcel Breyer, 2021, IWOCL.

Data mining on vast data sets as a cluster system benchmark

Dirk Pflüger, Hans-Joachim Bungartz, Alexander Heinecke, 2016, Concurr. Comput. Pract. Exp..

Emerging Architectures Enable to Boost Massively Parallel Data Mining Using Adaptive Sparse Grids

Dirk Pflüger, Alexander Heinecke, A. Heinecke, 2013, International Journal of Parallel Programming.

Spatially Adaptive Refinement

Dirk Pflüger, D. Pflüger, 2012 .

Lecture Notes in Computational Science and Engineering

Dirk Pflüger, Tamar Schlick, Michael Griebel, 2010 .

Computational Efficiency vs. Maintainability and Portability. Experiences with the Sparse Grid Code SG++

Dirk Pflüger, David Pfander, D. Pflüger, 2016, 2016 Fourth International Workshop on Software Engineering for High Performance Computing in Computational Science and Engineering (SE-HPCCSE).

PDEBENCH: An Extensive Benchmark for Scientific Machine Learning

Mathias Niepert, F. Alesiani, D. Pflüger, 2022, NeurIPS.

Surrogate-data-enriched Physics-Aware Neural Networks pdf

Dirk Pflüger, Bernard Haasdonk, Raphael Leiteritz, 2021, NLDL.

A Sparse-Grid-Based Out-of-Sample Extension for Dimensionality Reduction and Clustering with Laplacian Eigenmaps

Benjamin Peherstorfer, Dirk Pflüger, Hans-Joachim Bungartz, 2011, Australasian Conference on Artificial Intelligence.

Adaptive Sparse Grid Classification Using Grid Environments

Dirk Pflüger, Hans-Joachim Bungartz, Ioan Lucian Muntean, 2007, International Conference on Computational Science.

From piz daint to the stars: simulation of stellar mergers using high-level abstractions

Dirk Pflüger, Patrick Diehl, John Biddiscombe, 2019, SC.

Enabling unstructured domain decompositions for inhomogeneous short-range molecular dynamics in ESPResSo

Dirk Pflüger, Colin W. Glass, Steffen Hirschmann, 2019, The European Physical Journal Special Topics.

Towards Understanding Optimal Load-Balancing of Heterogeneous Short-Range Molecular Dynamics

Dirk Pflüger, Colin W. Glass, Steffen Hirschmann, 2016, 2016 IEEE 23rd International Conference on High Performance Computing Workshops (HiPCW).

Selected Recent Applications of Sparse Grids

Benjamin Peherstorfer, Dirk Pflüger, Hans-Joachim Bungartz, 2015 .

Adaptive Sparse Grid Techniques for Data Mining

Dirk Pflüger, Hans-Joachim Bungartz, Stefan Zimmer, 2006, HPSC.

Fast Insight into High-Dimensional Parametrized Simulation Data

Benjamin Peherstorfer, Dirk Pflüger, Hans-Joachim Bungartz, 2012, 2012 11th International Conference on Machine Learning and Applications.

B-splines on sparse grids for surrogates in uncertainty quantification

Dirk Pflüger, Fabian Franzelin, Michael F. Rehme, 2021, Reliab. Eng. Syst. Saf..

From Data to Uncertainty: An Efficient Integrated Data-Driven Sparse Grid Approach to Propagate Uncertainty

Dirk Pflüger, Fabian Franzelin, D. Pflüger, 2016 .

Non-intrusive Uncertainty Quantification with Sparse Grids for Multivariate Peridynamic Simulations

Dirk Pflüger, Patrick Diehl, Fabian Franzelin, 2015 .

Performance Evolution of Different SYCL Implementations based on the Parallel Least Squares Support Vector Machine Library

D. Pflüger, Marcel Breyer, Alexander Van Craen, 2023, International Workshop on OpenCL.

Recent Developments in the Theory and Application of the Sparse Grid Combination Technique

Dirk Pflüger, Markus Hegland, Peter E. Strazdins, 2016, Software for Exascale Computing.

Hybrid parallel solutions of the Black-Scholes PDE with the truncated combination technique

Dirk Pflüger, Janos Benk, D. Pflüger, 2012, 2012 International Conference on High Performance Computing & Simulation (HPCS).

PLSSVM - Parallel Least Squares Support Vector Machine

D. Pflüger, Marcel Breyer, Alexander Van Craen, 2022, Softw. Impacts.

A Comparison of SYCL, OpenCL, CUDA, and OpenMP for Massively Parallel Support Vector Machine Classification on Multi-Vendor Hardware

D. Pflüger, Marcel Breyer, Alexander Van Craen, 2022, IWOCL.

PLSSVM: A (multi-)GPGPU-accelerated Least Squares Support Vector Machine

D. Pflüger, Marcel Breyer, Alexander Van Craen, 2022, 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).

Bond-based peridynamics: a quantitative study of Mode I crack opening

Dirk Pflüger, Patrick Diehl, Fabian Franzelin, 2016, International Journal of Fracture.

Stellar Mergers with HPX-Kokkos and SYCL: Methods of using an Asynchronous Many-Task Runtime System with SYCL

D. Pflüger, Gregor Daiß, H. Kaiser, 2023, IWOCL.

From Task-Based GPU Work Aggregation to Stellar Mergers: Turning Fine-Grained CPU Tasks into Portable GPU Kernels

Dominic C. Marcello, D. Pflüger, Gregor Daiß, 2022, 2022 IEEE/ACM International Workshop on Performance, Portability and Productivity in HPC (P3HPC).

Option pricing with a direct adaptive sparse grid approach

Dirk Pflüger, Hans-Joachim Bungartz, Alexander Heinecke, 2012, J. Comput. Appl. Math..

Deep learning based surrogate modeling for thermal plume prediction of groundwater heat pumps

Miriam Schulte, D. Pflüger, Raphael Leiteritz, 2023, ArXiv.

A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps

Miriam Schulte, D. Pflüger, Raphael Leiteritz, 2022, ArXiv.

How to Avoid Trivial Solutions in Physics-Informed Neural Networks pdf

Dirk Pflüger, Raphael Leiteritz, D. Pflüger, 2021, ArXiv.

Project Report on “Load-Balancing for Large-Scale Soot Particle Agglomeration Simulations” (Reprint)

C. W. Glass, A. Kronenburg, D. Pflüger, 2021, High Performance Computing in Science and Engineering '20.

Towards High-Dimensional Computational Steering of Precomputed Simulation Data using Sparse Grids

Dirk Pflüger, Hans-Joachim Bungartz, Daniel Butnaru, 2011, ICCS.

EXAHD: An Exa-scalable Two-Level Sparse Grid Approach for Higher-Dimensional Problems in Plasma Physics and Beyond

Dirk Pflüger, Hans-Joachim Bungartz, Frank Jenko, 2014, Euro-Par Workshops.

Scalable Algorithms for the Solution of Higher-Dimensional PDEs

Dirk Pflüger, Mario Heene, D. Pflüger, 2016, Software for Exascale Computing.

Towards a Fault-Tolerant, Scalable Implementation of GENE

Dirk Pflüger, Hans-Joachim Bungartz, Christoph Kowitz, 2015 .

Global communication schemes for the numerical solution of high-dimensional PDEs

Dirk Pflüger, Riko Jacob, Philipp Hupp, 2016, Parallel Comput..

Visualization of fracture progression in peridynamics

Thomas Ertl, Dirk Pflüger, Steffen Frey, 2017, Comput. Graph..

Extraction of Fragments and Waves After Impact Damage in Particle-Based Simulations

Thomas Ertl, Dirk Pflüger, Steffen Frey, 2017 .

Modeling and Simulation - An Application-Oriented Introduction

Dirk Pflüger, Hans-Joachim Bungartz, Martin Buchholz, 2016, Springer undergraduate texts in mathematics and technology.

PDEBENCH: An Extensive Benchmark for Scientific Machine Learning

Mathias Niepert, F. Alesiani, D. Pflüger, 2022, NeurIPS.

Limiting Ranges of Function Values of Sparse Grid Surrogates

Dirk Pflüger, Fabian Franzelin, D. Pflüger, 2018 .

EXAHD: A Massively Parallel Fault Tolerant Sparse Grid Approach for High-Dimensional Turbulent Plasma Simulations

Michael Obersteiner, Dirk Pflüger, Hans-Joachim Bungartz, 2020, Software for Exascale Computing.

Load Balancing for Massively Parallel Computations with the Sparse Grid Combination Technique

Dirk Pflüger, Christoph Kowitz, Mario Heene, 2013, PARCO.

Extending a Highly Parallel Data Mining Algorithm to the Intel ® Many Integrated Core Architecture

Michael Klemm, Dirk Pflüger, Hans-Joachim Bungartz, 2011, Euro-Par Workshops.

Compact data structure and scalable algorithms for the sparse grid technique

Dirk Pflüger, Josef Weidendorfer, Daniel Butnaru, 2011, PPoPP '11.

Extending a Highly Parallel Data Mining

H. Bungartz, A. Bode, A. Heinecke, 2012 .

Stochastic Collocation with Hierarchical Extended B-Splines on Sparse Grids

Dirk Pflüger, Michael F. Rehme, D. Pflüger, 2021 .

Density Estimation with Adaptive Sparse Grids for Large Data Sets

Benjamin Peherstorfer, Dirk Pflüger, Hans-Joachim Bungartz, 2014, SDM.

Simulation software engineering: experiences and challenges

Dirk Pflüger, Stefan Wagner, Miriam Mehl, 2015, SE-HPCCSE@SC.

The Scalability-Efficiency/Maintainability-Portability Trade-Off in Simulation Software Engineering: Examples and a Preliminary Systematic Literature Review

Yang Wang, Dirk Pflüger, Stefan Wagner, 2016, 2016 Fourth International Workshop on Software Engineering for High Performance Computing in Computational Science and Engineering (SE-HPCCSE).