Reduced order isogeometric boundary element methods for CAD-integrated shape optimization in electromagnetic scattering

Chen, Leilei; Wang, Zhongwang; Lian, Haojie; Ma, Yujing; Meng, Zhuxuan; Li, Pei; Ding, Chensen; Bordas, Stéphane P.A.

doi:10.1016/j.cma.2023.116654

Cited by 38 publications

(9 citation statements)

References 37 publications

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“…where curl ν stands for the normal component of the surface curl defined and denoted by curl Γ := ∇ τ ×; specifically curl ν a := ν • curl Γ a ≡ ν • (∇ τ × a) for any vector field a = a(x, t). 4 Also, div Γ := ∇ τ • denotes the surface divergence.…”

Section: Discussionmentioning

confidence: 99%

“…Although the former is not in the scope of this article, it is capturing attentions as the artificial intelligence (AI) is growing rapidly, e.g. the deep learning with the electric and magnetic field integral equations [4]. However, if they could be formulated mathematically, the gradient-based methods would be more robust than the gradient-free methods.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An electromagnetic shape optimisation for perfectly electric conductors by the time-domain boundary integral equations

Takahashi

2024

Engineering with Computers

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This study proposes a shape optimisation framework for unsteady electromagnetic scattering problems on the basis of the time-domain boundary integral equation method, focusing on the perfectly electric conductors (PECs). The boundary-only formulation is ideal for treating a shape optimisation problem in exterior domains. However, the electromagnetic shape optimisation in concern has been unrealised with the boundary integral approach regardless of the fact that the boundarytype shape derivative has been known in the literature. The first contribution of the present study is to derive a novel expression of the shape derivative in terms of the surface current densities of the primary and adjoint problems, by considering that the surface current density is handled by usual integral equations methods. The second contribution is to clarify the integral representations and equations of the adjoint electromagnetic fields in terms of the reversal time. These theoretical achievements possess a high affinity with the standard spatial discretising approach (i.e. RWG basis) whenever the temporal basis is sufficiently smooth. The numerical experiments confirmed the reliability of the proposed shape optimisation methodology and indicated the capability to deal with scientific and engineering applications.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An electromagnetic shape optimisation for perfectly electric conductors by the time-domain boundary integral equations

Takahashi

2024

Engineering with Computers

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“…Refs. [27][28][29] adopted model order reduction methods combined with deep learning to accelerate the sampling procedure in uncertainty quantification.…”

Section: Uncertainty Estimation In Deep Learningmentioning

confidence: 99%

Uncertainty Quantification of Neural Reflectance Fields for Underwater Scenes

Lian,

Li,

Chen

et al. 2024

JMSE

Self Cite

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Neural radiance fields and neural reflectance fields are novel deep learning methods for generating novel views of 3D scenes from 2D images. To extend the neural scene representation techniques to complex underwater environments, beyond neural reflectance fields underwater (BNU) was proposed, which considers the relighting conditions of on-aboard light sources by using neural reflectance fields, and approximates the attenuation and backscatter effects of water with an additional constant. Because the quality of the neural representation of underwater scenes is critical to downstream tasks such as marine surveying and mapping, the model reliability should be considered and evaluated. However, current neural reflectance models lack the ability of quantifying the uncertainty of underwater scenes that are not directly observed during training, which hinders their widespread use in the field of underwater unmanned autonomous navigation. To address this issue, we introduce an ensemble strategy to BNU that quantifies cognitive uncertainty in color space and unobserved regions with the expectation and variance of RGB values and termination probabilities along the ray. We also employ a regularization method to smooth the density of the underwater neural reflectance model. The effectiveness of the present method is demonstrated in numerical experiments.

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“…Lastly, Bao et al [15] analyzed the large deformation mechanism in deep brittle rock tunnels based on the evolution of microcracks. The geometric finite element simulation of crack propagation under pressure by Chen Leilei et al provided some inspiration for the modeling and simulation of large deformation in this paper [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Mainly focused on the changes in the mechanical properties and mineral composition of rocks after water exposure, current researches rarely devote themselves to the relationship between soft rock deformation and the surrounding environment to establish models and field experiments for comparison and analysis, and study in a multidimensional way to develop a systematic and in-depth research on the relationship between the large deformation mechanism of mudstone and the macroscopic deformation behavior of seepage tunnels.…”

Section: Introductionmentioning

confidence: 99%

Research on intrusion and large arch bulge in lining structure for highway’s mudstone tunnel

Li,

Fu,

Liu

et al. 2024

Front. Phys.

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During the construction of a highway in northwest China, large deformation of mudstone caused severe deformation of and damage to side walls, initial support, and secondary lining to various extents. To reveal the causes of mudstone’s large deformation in the tunnels of this highway, a comprehensive study was conducted by using engineering geological survey, on-site monitoring and measurement, indoor rock mechanics test, numerical simulation, and macroscopic analysis. For the problem of large deformation of this highway’s tunnel section from YK209 + 500 m to YK210 + 030 m, the 3D finite difference method FLAC3D was used to simulate the large deformation of the wall rock and compare the deformation of the tunnel and the mechanical characteristics of the lining structure under different conditions by means of inverse analysis of the rheological characteristics of the mudstone and simulation of the softening of the mudstone in water. The research results provide a reference and basis for the construction design of similar mudstone tunnel projects. For the management of tunnel deformation, it is recommended to enhance the tunnel’s drainage measures, thereby mitigating the intensification of mudstone softening when exposed to water.

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Reduced order isogeometric boundary element methods for CAD-integrated shape optimization in electromagnetic scattering

Cited by 38 publications

References 37 publications

An electromagnetic shape optimisation for perfectly electric conductors by the time-domain boundary integral equations

An electromagnetic shape optimisation for perfectly electric conductors by the time-domain boundary integral equations

Uncertainty Quantification of Neural Reflectance Fields for Underwater Scenes

Research on intrusion and large arch bulge in lining structure for highway’s mudstone tunnel

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