| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0641368 |
| utime |
20251120112324.8 |
| mtime |
20251112235959.9 |
| SCOPUS |
105003168138 |
| WOS |
001529693400011 |
| DOI |
10.1007/978-3-031-85703-4_11 |
| title
(primary) (eng) |
Minimization of Nonlinear Energies in Python Using FEM and Automatic Differentiation Tools |
| specification |
| page_count |
15 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0641367 |
| ISBN |
978-3-031-85702-7 |
| ISSN |
0302-9743 |
| title
|
Lecture Notes in Computer Science |
| part_num |
15581 |
| page_num |
159-173 |
| publisher |
| place |
Berlin |
| name |
Springer Nature Switzerland AG |
| year |
2025 |
|
| editor |
| name1 |
Wyrzykowski |
| name2 |
R. |
|
| editor |
|
| editor |
|
| editor |
| name1 |
Karczewski |
| name2 |
K. |
|
|
| keyword |
nonlinear energy minimization |
| keyword |
autograd |
| keyword |
p-Laplacian |
| keyword |
Ginzburg-Landau model |
| keyword |
hyperelasticity |
| keyword |
finite elements |
| author
(primary) |
| ARLID |
cav_un_auth*0366821 |
| name1 |
Béreš |
| name2 |
Michal |
| institution |
UGN-S |
| full_dept (cz) |
Oddělení aplikované matematiky a informatiky & Oddělení IT4Innovations |
| full_dept (eng) |
Department of applied mathematics and computer science and Department IT4Innovations |
| full_dept |
Applied Mathematics and Computer Science & IT4Innovations |
| country |
CZ |
| fullinstit |
Ústav geoniky AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0292941 |
| name1 |
Valdman |
| name2 |
Jan |
| institution |
UTIA-B |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department |
MTR |
| full_dept |
Department of Decision Making Theory |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA24-10366S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0472839 |
|
| abstract
(eng) |
This contribution examines the capabilities of the Python ecosystem to solve nonlinear energy minimization problems, with a particular focus on transitioning from traditional MATLAB methods to Python's advanced computational tools, such as automatic differentiation. We demonstrate Python's streamlined approach to minimizing nonlinear energies by analyzing three problem benchmarks - the p-Laplacian, the Ginzburg-Landau model, and the Neo-Hookean hyperelasticity. This approach merely requires the provision of the energy functional itself, making it a simple and efficient way to solve this category of problems. The results show that the implementation is about ten times faster than the MATLAB implementation for large-scale problems. Our findings highlight Python's efficiency and ease of use in scientific computing, establishing it as a preferable choice for implementing sophisticated mathematical models and accelerating the development of numerical simulations. |
| action |
| ARLID |
cav_un_auth*0487577 |
| name |
International Conference of Parallel Processing and Applied Mathematics (PPAM 2024) /15./ |
| dates |
20240908 |
| mrcbC20-s |
20240911 |
| place |
Ostrava |
| country |
CZ |
|
| RIV |
BA |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10102 |
| reportyear |
2026 |
| num_of_auth |
2 |
| mrcbC47 |
UTAM-F 10000 10100 10102 |
| mrcbC52 |
2 4 X 4 4x 4 20251112132317.9 20251112132335.1 20251112132338.2 |
| presentation_type |
PR |
| mrcbC55 |
UTIA-B BA |
| inst_support |
RVO:68145535 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0371537 |
| confidential |
S |
| mrcbT16-q |
499 |
| mrcbT16-s |
0.606 |
| mrcbT16-y |
25.34 |
| mrcbT16-x |
1.17 |
| mrcbT16-3 |
102124 |
| mrcbT16-4 |
Q2 |
| arlyear |
2025 |
| mrcbTft |
\nSoubory v repozitáři: UGN_0641368.pdf |
| mrcbU14 |
105003168138 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
001529693400011 WOS |
| mrcbU63 |
cav_un_epca*0641367 Lecture Notes in Computer Science 15581 978-3-031-85702-7 0302-9743 1611-3349 159 173 PARALLEL PROCESSING AND APPLIED MATHEMATICS, PART III Berlin Springer Nature Switzerland AG 2025 |
| mrcbU67 |
Wyrzykowski R. 340 |
| mrcbU67 |
Dongarra J. 340 |
| mrcbU67 |
Deelman E. 340 |
| mrcbU67 |
Karczewski K. 340 |
|