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Jonas Schmitt

About Jonas Schmitt

Jonas Schmitt, With an exceptional h-index of 5 and a recent h-index of 5 (since 2020), a distinguished researcher at Friedrich-Alexander-Universität Erlangen-Nürnberg, specializes in the field of Automated Algorithm Design, Evolutionary Computation, Computational Science, Artificial Intelligence.

His recent articles reflect a diverse array of research interests and contributions to the field:

Automating the Design of Multigrid Methods with Evolutionary Program Synthesis

Evolving Nonlinear Multigrid Methods With Grammar-Guided Genetic Programming

Evolving generalizable multigrid-based helmholtz preconditioners with grammar-guided genetic programming

Genetic programming for iterative numerical methods

EvoStencils: a grammar-based genetic programming approach for constructing efficient geometric multigrid methods

tinyMD: Mapping molecular dynamics simulations to heterogeneous hardware using partial evaluation

tinyMD: A Portable and Scalable Implementation for Pairwise Interactions Simulations

Constructing efficient multigrid solvers with genetic programming

Jonas Schmitt Information

University

Position

___

Citations(all)

76

Citations(since 2020)

68

Cited By

26

hIndex(all)

5

hIndex(since 2020)

5

i10Index(all)

4

i10Index(since 2020)

2

Email

University Profile Page

Google Scholar

Jonas Schmitt Skills & Research Interests

Automated Algorithm Design

Evolutionary Computation

Computational Science

Artificial Intelligence

Top articles of Jonas Schmitt

Automating the Design of Multigrid Methods with Evolutionary Program Synthesis

arXiv preprint arXiv:2312.14875

2023/12/22

Jonas Schmitt
Jonas Schmitt

H-Index: 3

Evolving Nonlinear Multigrid Methods With Grammar-Guided Genetic Programming

2023/7/15

Dinesh Parthasarathy
Dinesh Parthasarathy

H-Index: 0

Jonas Schmitt
Jonas Schmitt

H-Index: 3

Evolving generalizable multigrid-based helmholtz preconditioners with grammar-guided genetic programming

2022/7/8

Jonas Schmitt
Jonas Schmitt

H-Index: 3

Genetic programming for iterative numerical methods

Genetic Programming and Evolvable Machines

2022/6

Jonas Schmitt
Jonas Schmitt

H-Index: 3

Franz Rothlauf
Franz Rothlauf

H-Index: 18

EvoStencils: a grammar-based genetic programming approach for constructing efficient geometric multigrid methods

Genetic Programming and Evolvable Machines

2021/12

Jonas Schmitt
Jonas Schmitt

H-Index: 3

Sebastian Kuckuk
Sebastian Kuckuk

H-Index: 9

tinyMD: Mapping molecular dynamics simulations to heterogeneous hardware using partial evaluation

Journal of Computational Science

2021/9/1

Jonas Schmitt
Jonas Schmitt

H-Index: 3

Sebastian Eibl
Sebastian Eibl

H-Index: 6

Jan Eitzinger
Jan Eitzinger

H-Index: 16

Sebastian Hack
Sebastian Hack

H-Index: 19

tinyMD: A Portable and Scalable Implementation for Pairwise Interactions Simulations

arXiv preprint arXiv:2009.07400

2020/9/16

Jonas Schmitt
Jonas Schmitt

H-Index: 3

Sebastian Eibl
Sebastian Eibl

H-Index: 6

Jan Eitzinger
Jan Eitzinger

H-Index: 16

Roland Leißa
Roland Leißa

H-Index: 8

Sebastian Hack
Sebastian Hack

H-Index: 19

Constructing efficient multigrid solvers with genetic programming

2020/6/25

Jonas Schmitt
Jonas Schmitt

H-Index: 3

Sebastian Kuckuk
Sebastian Kuckuk

H-Index: 9

Exastencils: advanced multigrid solver generation

2020

Christian Lengauer
Christian Lengauer

H-Index: 18

Sven Apel
Sven Apel

H-Index: 43

Matthias Bolten
Matthias Bolten

H-Index: 14

Ulrich Rüde
Ulrich Rüde

H-Index: 30

Jürgen Teich
Jürgen Teich

H-Index: 32

Frank Hannig
Frank Hannig

H-Index: 19

Alexander Grebhahn
Alexander Grebhahn

H-Index: 10

Stefan Groth
Stefan Groth

H-Index: 6

Sebastian Kuckuk
Sebastian Kuckuk

H-Index: 9

Christian Schmitt
Christian Schmitt

H-Index: 9

Jonas Schmitt
Jonas Schmitt

H-Index: 3

See List of Professors in Jonas Schmitt University(Friedrich-Alexander-Universität Erlangen-Nürnberg)