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Titlebook: Large-Scale Scientific Computations; 14th International C Ivan Lirkov,Svetozar Margenov Conference proceedings 2024 The Editor(s) (if appli

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Machine Learning Algorithms for Parameter Identification for Reactive Flow in Porous Mediasion dominated and reaction dominated regimes [.,.,.,.]. Pore scale reactive transport was studied, breakthrough curves were the additional information used in identifying the parameters in Henry or Langmuir isotherms. All methods were time consuming, requiring multiple solution of the direct proble
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Randomized Symplectic Model Order Reduction for Hamiltonian Systemsia Model Order Reduction (MOR). Recently, symplectic methods like the complex singular value decomposition (cSVD) or the SVD-like decomposition have been developed for preserving Hamiltonian structure during MOR. In this contribution, we show how symplectic structure preserving basis generation can
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Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modelingicient approximation of input-output maps that are governed by parametrized partial differential equations. This adaptive approach combines a full order model, a reduced order model and a machine-learning model. In this contribution, we extend the approach by leveraging novel kernel models for the m
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Numerical Determination of Source from Point Observation in a Time-Fractional Boundary-Value Problemtion problem defined on disjoint intervals. The fractional derivative is in Caputo sense with different fractional order on each of the subintervals. We propose algorithms, based on decomposition with respect to the sources on time adaptive mesh. Numerical tests illustrate the efficiency of the prop
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Owe Axelsson,Roman Kohut,Maya NeytchevaWandel bieten. Für eine erste Auseinandersetzung mit der Thematik Wandel/Veränderung möchte ich an dieser Stelle auf Türk verweisen. Dieser definiert Veränderung respektive Wandel folgendermaßen: „‚Veränderung‘ heißt … zunächst einmal schlicht: ein beobachtetes Merkmal weist im Vergleich seiner Ausp
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