Measurement-based stochastic models of biological materials

Krupa, Artur; Sawicki, Bartosz

doi:10.1109/cpee.2017.8093068

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Authors constructed a device to measure the electrical conductivity of solid biological samples [6]. As it is presented on Fig.…”

Section: Stochastic Materials Parametersmentioning

confidence: 99%

“…Methods for solving stochastic problems could be divided into two groups: intrusivewhich redefine whole mathematical description (e.g. Stochastic Finite Element Method [9]), ITM Web of Conferences 29, 01008 (2019) https://doi.org/10.1051/itmconf/20192901008 ICCMAE 2018 Figure 1: Picture and scheme of device constructed to measure electrical conductivity of solid materials with biological origin [6] and non-intrusive where original deterministic solver is used as a part of the simulation algorithm. Intrusive methods are attractive and efficient for simple problems, nevertheless for large-scale simulations non-intrusive approach shows its benefits [5].…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Modeling of Electrical Field in Potato Tuber using Polynomial Chaos Expansion

Sawicki¹,

Krupa²

2019

ITM Web Conf.

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The paper deals with numerical modeling of objects with a natural origin. The stochastic approach based on description using random variables allows processing such challenges. The Monte-Carlo methods are known a tool for simulations containing stochastic parameters however, they require significant computational power to obtain stable results. Authors compare Monte- Carlo with more advanced Polynomial Chaos Expansion (PCE) method. Both statistical tools have been applied for simulation of the electric field used in ohmic heating of potato tuber probes. Results indicate that PCE is remarkably faster, however, it simplifies some probabilistic features of the solution.

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“…Authors constructed a device to measure the electrical conductivity of solid biological samples [6]. As it is presented on Fig.…”

Section: Stochastic Materials Parametersmentioning

confidence: 99%