A multi-GPU accelerated solver for the three-dimensional two-phase incompressible Navier-Stokes equations. - Michael Griebel; Peter Zaspel (Comput. Sci. Res. Dev., 2010)

Massively Parallel Fluid Simulations on Amazon's HPC Cloud. - Peter Zaspel; Michael Griebel (NCCA, 2011)

Photorealistic visualization and fluid animation: coupling of Maya with a two-phase Navier-Stokes fluid solver. - Peter Zaspel; Michael Griebel (Comput. Vis. Sci., 2011)

EXAHD: An Exa-scalable Two-Level Sparse Grid Approach for Higher-Dimensional Problems in Plasma Physics and Beyond. - Dirk Pflüger; Hans-Joachim Bungartz; Michael Griebel; Frank Jenko; Tilman Dannert; Mario Heene; Christoph Kowitz; Alfredo Parra-Hinojosa; Peter Zaspel (Euro-Par Workshops (2), 2014)

Uncertainty Quantification and High Performance Computing (Dagstuhl Seminar 16372). - Vincent Heuveline; Michael Schick; Clayton G. Webster; Peter Zaspel (Dagstuhl Reports, 2016)

Algorithmic patterns for H-matrices on many-core processors. - Peter Zaspel (CoRR, 2017)

A scalable H-matrix approach for the solution of boundary integral equations on multi-GPU clusters. - Helmut Harbrecht; Peter Zaspel (CoRR, 2018)

On the Algebraic Construction of Sparse Multilevel Approximations of Elliptic Tensor Product Problems. - Helmut Harbrecht; Peter Zaspel (J. Sci. Comput., 2019)

Algorithmic Patterns for $$ mathcal {H}$$ H -Matrices on Many-Core Processors. - Peter Zaspel (J. Sci. Comput., 2019)

Towards data-driven filters in Paraview. - Drishti Maharjan; Peter Zaspel (CoRR, 2021)

Multi-Fidelity Machine Learning for Excited State Energies of Molecules. - Vivin Vinod; Sayan Maity; Peter Zaspel; Ulrich Kleinekathöfer (CoRR, 2023)

CheMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules. - Vivin Vinod; Peter Zaspel (CoRR, 2024)

Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes. - Peter Zaspel; Michael Günther 005 (CoRR, 2024)

Benchmarking Data Efficiency in Δ-ML and Multifidelity Models for Quantum Chemistry. - Vivin Vinod; Peter Zaspel (CoRR, 2024)

Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies. - Vivin Vinod; Peter Zaspel (CoRR, 2024)

Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials. - Matthias Holzenkamp; Dongyu Lyu; Ulrich Kleinekathöfer; Peter Zaspel (CoRR, 2024)

Assessing non-nested configurations of multifidelity machine learning for quantum-chemical properties. - Vivin Vinod; Peter Zaspel (Mach. Learn. Sci. Technol., 2024)