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Enregistrement W4405739188 · doi:10.1016/j.cpc.2024.109476

TrussMe-Fem: A toolbox for symbolic-numerical analysis and solution of structures

2024· article· en· W4405739188 sur OpenAlexaboutno aff
Davide Stocco, Matteo Tomasi, Enrico Bertolazzi

Notice bibliographique

RevueComputer Physics Communications · 2024
Typearticle
Langueen
DomaineMaterials Science
ThématiqueHigh-Velocity Impact and Material Behavior
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésToolboxFinite element methodComputer scienceSymbolic data analysisNumerical analysisAlgebra over a fieldApplied mathematicsComputational scienceMathematicsTheoretical computer scienceMathematical analysisProgramming languageStructural engineeringPure mathematicsEngineering

Résumé

récupéré en direct d'OpenAlex

Structural mechanics is pivotal in comprehending how structures respond to external forces and imposed displacements. Typically, the analysis of structures is performed numerically using the direct stiffness method, which is an implementation of the finite element method. This method is commonly associated with the numerical solution of large systems of equations. However, the underlying theory can also be conveniently used to perform the analysis of structures either symbolically or in a hybrid symbolic-numerical fashion. This approach is useful to mitigate the computational burden as the obtained partial or full symbolic solution can be simplified and used to generate lean code for efficient simulations. Nonetheless, the symbolic direct stiffness method is also useful for model reduction purposes, as it allows the derivation of small-scale models that can be used for diminishing simulation time. Despite the mentioned advantages, symbolic computation carries intrinsically complex operations. In particular, the symbolic solution of large linear systems of equations is hard to compute, and it may not always be available due to software capabilities. This paper introduces a toolbox named TrussMe-Fem , whose implementation is based on the direct stiffness method. TrussMe-Fem leverages Maple ®'s symbolic computation and Matlab ®'s numerical capabilities for symbolic and hybrid symbolic-numerical analyses and solutions of structures. Efficient code generation is also possible by exploiting the simplification of the problem's expressions. The challenges posed by symbolic computation on the solution of large linear systems are addressed by introducing novel routines for the symbolic matrix factorization with the hierarchical representation of large expressions. For this purpose, the TrussMe-Fem toolbox optionally uses the Lem and Last Maple ® packages, which are also available as open-source software. Program Title: TrussMe-Fem CPC Library link to program files: https://doi.org/10.17632/m59fyw5hs4.1 Developer's repository link: https://github.com/StoccoDavide/TrussMe-FEM – Optional dependencies: Lem https://github.com/StoccoDavide/LEM , Last https://github.com/StoccoDavide/LAST Licensing provisions: BSD 3-clause. Programming language: Maple ®, Matlab ®. Supplementary material: Usage examples for the TrussMe-Fem toolbox, Lem and Last Maple ® packages. Nature of problem: Structural mechanics is a branch of mechanics that studies the behavior of structures subjected to loads and displacements. Typically, the analysis of the deformations and internal reactions is performed numerically using the finite element method. The plain numerical solution, however, is not always suitable for optimization problems. With the recent advances in symbolic computation, it is possible to perform the analysis of structures either symbolically or in a hybrid symbolic-numerical fashion. This approach is useful for enhancing the optimization process, as the derivatives of the solution with respect to the optimization parameters can be easily calculated. Nonetheless, lean code for efficient simulations can be generated by exploiting the simplification of the problem's symbolic expressions. Solution method: We combine the symbolic computation capabilities of Maple ® with the numerical performance of Matlab ® to develop a toolbox for the symbolic or hybrid symbolic-numerical analysis and solution of structures. The toolbox is based on the direct stiffness method and allows for modeling, analyzing, and solving structures. Depending on the symbolic kernel capability, available computation time, and problem complexity, the symbolic solution can be obtained in closed or numerical form. In both cases, a Matlab ® class can be generated to efficiently evaluate the symbolic solution or to numerically solve the problem. During the code generation, model inputs and class internal data are appropriately mapped to the generated code. Additional comments including restrictions and unusual features: The symbolic inversion of large matrices is an intrinsically complex task that may not always be carried out due to the expression swell phenomenon. Such a phenomenon causes the size of the expressions to significantly increase during the computation [1] and large CPU times are required to complete the matrix inversion tasks. As a consequence, the symbolic solution of large linear systems is not always available. For this reason, the toolbox resorts to symbolic matrix factorization techniques [2]. However, the symbolic decomposition may also be slowed down by the expression swell phenomenon [3]. In this case, the only viable option is to resort to the numerical solution of the linear system. [1] W. Zhou, Symbolic computation techniques for solving large expression problems from mathematics and engineering, Ph.D. thesis, University of Western Ontario, 2007. [2] J. Carette, W. Zhou, D. J. Jeffrey, M.B. Monagan, Linear algebra using Maple's LargeExpressions package, in: Proceedings of Maple Conference, 2006, pp. 14–25. [3] W. Zhou, J. Carette, D.J. Jeffrey, M.B. Monagan, Hierarchical representations with signatures for large expression management, in: Artificial Intelligence and Symbolic Computation: 8th International Conference, AISC 2006 Beijing, China, September 20-22, 2006 Proceedings 8, 2006, pp. 254–268. Springer.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Logiciel · Signal consensuel: aucune
Score de désaccord entre enseignants0,041
Score d'incertitude au seuil0,136

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,002
Science ouverte0,0030,002
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0410,020

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,043
Tête enseignante GPT0,332
Écart entre enseignants0,288 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreLogiciel

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2024
Routes d'admission1
Résumé présentoui

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