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Adaptive simulation of wave propagation problems including dislocation sources and random media

Hassan YOUSEFI, Jamshid FARJOODI, Iradj MAHMOUDZADEH KANI

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 5, Pages 1054-1081 doi: 10.1007/s11709-019-0536-4

Abstract: An adaptive Tikhonov regularization is integrated with an h-adaptive grid-based scheme for simulationThe Tikhonov method is adapted by a newly-proposed detector based on the MINMOD limiters and the gridsDue to developing of non-dissipative spurious oscillations, numerical stability is guaranteed by the Tikhonovregularization acting as a post-processor on irregular grids.To preserve waves of small magnitudes, an adaptive regularization is utilized: using of smaller amount

Keywords： adaptive wavelet adaptive smoothing discontinuous solutions stochastic media spurious oscillations Tikhonovregularization minmod limiter

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A regularization scheme for explicit level-set XFEM topology optimization

Markus J. GEISS, Jorge L. BARRERA, Narasimha BODDETI, Kurt MAUTE

Frontiers of Mechanical Engineering 2019, Volume 14, Issue 2, Pages 153-170 doi: 10.1007/s11465-019-0533-2

Abstract: Regularization of the level-set (LS) field is a critical part of LS-based topology optimization (TO)This paper introduces a novel LS regularization approach based on a signed distance field (SDF) which

Keywords： level-set regularization explicit level-sets XFEM CutFEM topology optimization heat method signed distance

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Non-convex sparse optimization-based impact force identification with limited vibration measurements

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 3, doi: 10.1007/s11465-023-0762-2

Abstract: MJX-TeXAtom-ORD">ℓ2 regularizationMJX-TeXAtom-ORD">ℓ2 regularizationℓ1 sparse regularizationTo alleviate such limitations, a novel non-convex sparse regularization method that uses the non-convexResults indicate that compared with other existing regularization methods, the

Keywords： impact force identification inverse problem sparse regularization under-determined condition alternating

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Short-term Load Forecasting Using Neural Network

Luo Mei

Strategic Study of CAE 2007, Volume 9, Issue 5, Pages 77-80

Abstract: Bayesian regularization can overcome the over fitting and improve the generalization of ANN.

Keywords： short-term load forecasting(STLF) ANN Levenberg-Marquardt Bayesian regularization optimized algorithms

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Asystematic review of structured sparse learning Review

Lin-bo QIAO, Bo-feng ZHANG, Jin-shu SU, Xi-cheng LU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 4, Pages 445-463 doi: 10.1631/FITEE.1601489

Abstract: High dimensional data arising from diverse scientific research fields and industrial development have led to increased interest in sparse learning due to model parsimony and computational advantage. With the assumption of sparsity, many computational problems can be handled efficiently in practice. Structured sparse learning encodes the structural information of the variables and has been quite successful in numerous research fields. With various types of structures discovered, sorts of structured regularizations have been proposed. These regularizations have greatly improved the efficacy of sparse learning algorithms through the use of specific structural information. In this article, we present a systematic review of structured sparse learning including ideas, formulations, algorithms, and applications. We present these algorithms in the unified framework of minimizing the sum of loss and penalty functions, summarize publicly accessible software implementations, and compare the computational complexity of typical optimization methods to solve structured sparse learning problems. In experiments, we present applications in unsupervised learning, for structured signal recovery and hierarchical image reconstruction, and in supervised learning in the context of a novel graph-guided logistic regression.

Keywords： Sparse learning Structured sparse learning Structured regularization