Nonlinear Parametid Model Order Reduction
Model order reduction techniques are a powerful tool to ease the computational burden of simulating complex systems. By reducing the dimensionality of high-fidelity models, while preserving essential system dynamics, model order reduction enables faster and more efficient simulations without compromising accuracy.
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On Stability And Model Order Reduction Of Perturbed Nonlinear Neural Networks
Marissa Condon, Georgi G. Grahovski
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Cytosolically synthesized chloroplast preproteins are translocated across the outer and inner envelope membranes through translocons called TOC and TIC, respectively. In green algae and plants, the TIC core is composed of essential membrane proteins, Tic12, Tic20, and Tic214.
Mengyi Li, Xueyang Zhao, Masato Nakai
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Optimized PID controller and model order reduction of reheated turbine for load frequency control using teaching learning-based optimization. [PDF]
Singh A +4 more
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A robust computational technique for model order reduction of two-time-scale discrete systems via genetic algorithms. [PDF]
Alsmadi OM, Abo-Hammour ZS.
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MiR‐513a promotes human erythroid differentiation by modulating c‐Jun
During early human erythropoiesis, miR‐513a promoted erythroid differentiation in primary human CD34+ hematopoietic stem‐progenitor cells and human TF‐1 erythroleukemic cells by indirectly decreasing c‐Jun and phospho‐c‐Jun expression, which are associated with increased GATA1 expression.
MinJung Kim +11 more
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Editorial: Advanced materials modeling combining model order reduction and data science
Chady Ghnatios +3 more
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A partitioned model order reduction approach to rationalise computational expenses in nonlinear fracture mechanics. [PDF]
Kerfriden P +4 more
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The use of POD–DEIM model order reduction for the simulation of nonlinear hygrothermal problems [PDF]
Tianfeng Hou +3 more
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