A total lagrangian stochastic finite element method shows improved hyperelastic analysis with uncertainties, indicating efficiency in high-dimensional random inputs.
This article presents a total Lagrangian stochastic finite element method to solve hyperelastic problems with uncertainties. Similar to deterministic hyperelastic analysis, prescribed external forces or boundary values are applied through a series of load steps. By using a stochastic Newton‐Raphson method, the stochastic hyperelastic analysis at each load step is linearized as a series of linear stochastic equations. To avoid the use of stochastic configurations from intermediate load steps, all analyses are performed on the initial configuration, thus referred to as a total Lagrangian method. Each stochastic increment of the stochastic solution is then approximated as the product of a random variable and a deterministic vector, and they are solved through a dedicated iteration. Specifically, the deterministic vector is solved using linear deterministic equations, and the corresponding random variable is calculated through one‐dimensional stochastic algebraic equations that can be solved efficiently using a sample‐based strategy, even for very high‐dimensional random inputs. In this way, the proposed method avoids the curse of dimensionality to a great extent. 2D and 3D numerical examples with up to 100 stochastic dimensions demonstrate the promising performance of the proposed method.
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Zheng et al. (2025) studied this question.
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