SoftFEM: the Soft Finite Element Method

Peña Sánchez, José María and Latorre De La Fuente, Antonio and Jérusalem, Antoine (2019). SoftFEM: the Soft Finite Element Method. "International Journal for Numerical Methods in Engineering", v. 118 (n. 10); pp. 606-630. ISSN 1097-0207.


Title: SoftFEM: the Soft Finite Element Method
  • Peña Sánchez, José María
  • Latorre De La Fuente, Antonio
  • Jérusalem, Antoine
Item Type: Article
Título de Revista/Publicación: International Journal for Numerical Methods in Engineering
Date: February 2019
ISSN: 1097-0207
Volume: 118
Freetext Keywords: Finite element method, Heuristic optimization, Soft computing, SoftFEM, Solid mechanics
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Arquitectura y Tecnología de Sistemas Informáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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While the finite element method (FEM) has now reached full maturity both in academy and industry, its use in optimization pipelines remains either computationally intensive or cumbersome. In particular, currently used optimization schemes leveraging FEM still require the choice of dedicated optimization algorithms for a specific design problem, and a “black box” approach to FEM‐based optimization remains elusive. To this end, we propose here an integrated finite element‐soft computing method, ie, the soft FEM (SoftFEM), which integrates a finite element solver within a metaheuristic search wrapper. To illustrate this general method, we focus here on solid mechanics problems. For these problems, SoftFEM is able to optimize geometry changes and mechanistic measures based on geometry constraints and material properties inputs. From the optimization perspective, the use of a fitness function based on finite element calculation imposes a series of challenges. To bypass the limitations in search capabilities of the usual optimization techniques (local search and gradient‐based methods), we propose, instead a hybrid self adaptive search technique, the multiple offspring sampling (MOS), combining two metaheuristics methods: one population‐based differential evolution method and a local search optimizer. The formulation coupling FEM to the optimization wrapper is presented in detail and its flexibility is illustrated with three representative solid mechanics problems. More particularly, we propose here the MOS as the most versatile search algorithm for SoftFEM. A new method for the identification of nonfully determined parameters is also proposed.

Funding Projects

Government of SpainTIN2017-83132-C2-2-RUnspecifiedUniversidad Politécnica de MadridVisualización analítica aplicada
Universidad Politécnica de MadridPINV-18-XEOGHQ-19-4QTEBPUnspecifiedUnspecifiedUnspecified

More information

Item ID: 66986
DC Identifier:
OAI Identifier:
DOI: 10.1002/nme.6029
Official URL:
Deposited by: Memoria Investigacion
Deposited on: 11 May 2021 09:11
Last Modified: 11 May 2021 09:11
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