On the Computation of Tensor Functions under Tensor‐Tensor Multiplications with Linear Maps
Numerical Linear Algebra with Applications, Volume 33, Issue 3, June 2026.ABSTRACT In this paper, we study the computation of both algebraic and non‐algebraic tensor functions under the tensor‐tensor multiplication with linear maps. In the case of algebraic tensor functions, we prove that the asymptotic exponent of both the tensor‐tensor multiplication and the tensor polynomial evaluation problem under this multiplication is
Jeong‐Hoon Ju, Susana López‐Moreno
wiley +1 more source
Challenges and alternatives to empirical orthogonal functions for earth system data. [PDF]
Sci RepShields CA +6 more
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Building a Digital Twin for Material Testing: Model Reduction and Data Assimilation
Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.ABSTRACT The rapid advancement of industrial technologies, data collection, and handling methods has paved the way for the widespread adoption of digital twins (DTs) in engineering, enabling seamless integration between physical systems and their virtual counterparts.
Rubén Aylwin +5 more
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Fourth-order kinematic analysis: Advanced decomposition methods for particle motion in modified orthogonal frame. [PDF]
PLoS OneAlghamdi F, Elsharkawy A.
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Nonlinear Model Order Reduction on Polynomial Manifolds for Computational Homogenisation Problems
Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.ABSTRACT Model order reduction (MOR) techniques utilising nonlinear approximation spaces can search for solutions to computational homogenisation problems on low‐dimensional approximation spaces. In combination with hyperreduction techniques, this allows for computations on representative volume elements (RVEs) to be accelerated by multiple orders of ...
Erik Faust, Lisa Scheunemann
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Sparse Reconstruction of Pressure Field for Wedge Passive Fluidic Thrust Vectoring Nozzle. [PDF]
Sensors (Basel)Huang Z, Gu Y, Xu Q, Li L.
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Toward an Efficient Shifted Cholesky QR for Applications in Model Order Reduction Using pyMOR
Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.ABSTRACT Many model order reduction (MOR) methods rely on the computation of an orthonormal basis of a subspace onto which the large full order model is projected. Numerically, this entails the orthogonalization of a set of vectors. The nature of the MOR process imposes several requirements for the orthogonalization process.
Maximilian Bindhak +2 more
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Detecting Beta-Amyloid Plaque via Low Rank Based Orthogonal Projection and Spatial-Spectrum Detector Using High-Resolution Quantitative Susceptibility Mapping for Preclinical Studies. [PDF]
IEEE Trans Biomed EngChen J +10 more
europepmc +1 more source
Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.ABSTRACT The use of parametric model order reduction (pMOR) by matrix interpolation enables efficient simulation of large‐scale finite element (FE) models in multi‐query applications such as optimization and uncertainty quantification. In this method, high‐fidelity systems are sampled, individually reduced by projection‐based model order reduction, and
Sebastian Resch‐Schopper +1 more
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Transmission line fault detection and classification using bi-orthogonal wavelet transform (5.5) based signal decomposition. [PDF]
Sci RepChothani N +7 more
europepmc +1 more source