Evolutionary methods in inverse problems of engineering mechanics

Burczyński, Tadeusz; Beluch, Witold; Długosz, Adam; Orantek, P.; Nowakowski, M.

doi:10.1016/b978-008043693-7/50131-8

Cited by 14 publications

(6 citation statements)

References 2 publications

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“…This work is an extension of previous papers in which the coupling of EA and BEM has been used in generalized shape optimization of elastic structures , optimization of cracked structures , shape optimization of elastoplastic structures (Burczyński and Kuś 2000a, b) and identification of voids and cracks (Burczyński et al 2000). The present paper is an extension of previous works on the thermoelastic problems (Burczyński and Długosz 2000a, b, c) and the following problems are considered: (i) shape optimization of the structure for various thermomechanical criteria with upper bound on the volume of the structure, (ii) inverse problems of finding an optimal distribution of temperature on the boundary (boundary condition) for minimum of a displacement functional, and (iii) identification of the unknown boundary.…”

Section: Introductionmentioning

confidence: 94%

Evolutionary optimization in thermoelastic problems using the boundary element method

Burczyński¹,

Długosz²

2002

Computational Mechanics

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The paper is devoted to application of evolutionary algorithms and the boundary element method to shape optimization of structures for various thermomechanical criteria, inverse problems of finding an optimal distribution of temperature on the boundary and identification of unknown boundary. Design variables are specified by Bezier curves. Several numerical examples of evolutionary computation are presented.

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Section: Introductionmentioning

confidence: 94%

Evolutionary optimization in thermoelastic problems using the boundary element method

Burczyński¹,

Długosz²

2002

Computational Mechanics

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“…Solving inverse problems [17] is of paramount importance to our society. It is essential in, among others, most areas of engineering (see, e.g., [3,5]), health (see, e.g. [1]), military operations (see, e.g., [4]) and energy production (see, e.g.…”

Section: Introductionmentioning

confidence: 99%

Design of Loss Functions for Solving Inverse Problems Using Deep Learning

Rivera

Pardo

Alberdi

2020

Lecture Notes in Computer Science

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Solving inverse problems is a crucial task in several applications that strongly affect our daily lives, including multiple engineering fields, military operations, and/or energy production. There exist different methods for solving inverse problems, including gradient based methods, statistics based methods, and Deep Learning (DL) methods. In this work, we focus on the latest. Specifically, we study the design of proper loss functions for dealing with inverse problems using DL. To do this, we introduce a simple benchmark problem with known analytical solution. Then, we propose multiple loss functions and compare their performance when applied to our benchmark example problem. In addition, we analyze how to improve the approximation of the forward function by: (a) considering a Hermite-type interpolation loss function, and (b) reducing the number of samples for the forward training in the Encoder-Decoder method. Results indicate that a correct design of the loss function is crucial to obtain accurate inversion results.

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“…For instance, interior cracks were detected in a specimen from minimization of a cost function for the error between expected and measured acoustic potential and flux values at the surface [5], and subsurface cavities were identified using iterations that were based on over-specification of boundary data at the surfaces [6]. Also, Burczynski et al [7] coupled the boundary integrals with evolutionary-type algorithms (EA) for use in identification problems involving cracks and voids in various structures.…”

Section: Introductionmentioning

confidence: 99%