Results 41 to 50 of about 1,699 (225)
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
Advanced Intelligent Discovery, EarlyView.This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
L-structure least squares solutions of reduced biquaternion matrix equations with applications [PDF]
, 2023 This paper presents a framework for computing the structure-constrained least squares solutions to the generalized reduced biquaternion matrix equations (RBMEs). The investigation focuses on three different matrix equations: a linear matrix equation with
Bhadala, Neha, Ahmad, Sk. Safique
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Advanced Intelligent Discovery, EarlyView.We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi +3 more
wiley +1 more source
Special least squares solutions of the reduced biquaternion matrix equation with applications [PDF]
, 2023 This paper presents an efficient method for obtaining the least squares Hermitian solutions of the reduced biquaternion matrix equation $(AXB, CXD) = (E, F )$. The method leverages the real representation of reduced biquaternion matrices. Furthermore, we
Bhadala, Neha, Ahmad, Sk. Safique
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Asian Journal of Control, EarlyView.Abstract The linear‐quadratic regulator (LQR) problem of optimal control of an uncertain discrete‐time linear system (DTLS) is revisited in this paper from the perspective of Tikhonov regularization. We show that an optimally chosen regularization parameter reduces, compared to the classical LQR, the values of a scalar error function, as well as the ...
Fernando Pazos, Amit Bhaya
wiley +1 more source
On solution uniqueness of elliptic boundary value problems [PDF]
, 2009 In this paper, we consider the problem of solution uniqueness for the second order elliptic boundary value problem, by looking at its finite element or finite difference approximations.
Li, Zi-Cai +3 more
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Broad Skills, Smaller Footprints? Generalist CEOs and Firm Biodiversity Exposure
Business Strategy and the Environment, EarlyView.ABSTRACT We examine whether CEO generalist skills predict firm‐level biodiversity risk exposure, an emerging strategic and financial concern. Using the biodiversity indicator developed based on textual analysis and the General Ability Index, we find that firms led by generalist CEOs are associated with significantly lower disclosed biodiversity risk ...
Hafiz Hoque +2 more
wiley +1 more source
Solving forward and inverse Helmholtz equations via controllability methods [PDF]
, 2019 Waves are useful for probing an unknown medium by illuminating it with a source. To infer the characteristics of the medium from (boundary) measurements, for instance, one typically formulates inverse scattering problems in frequency domain as a PDE ...
Tang, Jet Hoe
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A partial envelope approach for modelling multivariate spatial‐temporal data
Canadian Journal of Statistics, EarlyView.Abstract In the new era of big data, modelling multivariate spatial‐temporal data is a challenging task due to both the high dimensionality of the features and complex associations among the responses across different locations and time points.
Reisa Widjaja +3 more
wiley +1 more source
ADVANCED NUMERICAL METHODS FOR THE DYNAMIC OPTIMISATION OF MECHANICAL COMPONENTS [PDF]
, 2009 This PhD thesis concerns the development and assessment of innovative methodologies for simulating and improving the dynamic behaviour of mechanical components.
Venturi, Valerio, Venturi, V.
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