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Model reduction: Why it is possible and how it can potentially help to control swarms of Unmanned Arial Vehicles (UAVs).

, , , and . NAFIPS/WConSC, page 1-6. IEEE, (2015)

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Ön-the-fly" Parameter Identification for Dynamic Systems Control, Using Interval Computations and Reduced-Order Modeling., , and . NAFIPS, volume 648 of Advances in Intelligent Systems and Computing, page 293-299. Springer, (2017)Solving Large Dynamical Systems by Constraint Sampling., , , , , and . WEA, volume 1052 of Communications in Computer and Information Science, page 3-15. Springer, (2019)Fuzzy techniques provide a theoretical explanation for the heuristic ℓp-regularization of signals and images., , , , and . FUZZ-IEEE, page 1323-1327. IEEE, (2016)Derivation of Louisville-Bratu-Gelfand Equation from Shift- or Scale-Invariance., , and . IFSA/NAFIPS, volume 1000 of Advances in Intelligent Systems and Computing, page 813-819. Springer, (2019)How to speed up software migration and modernization: Successful strategies developed by precisiating expert knowledge., , , and . NAFIPS/WConSC, page 1-6. IEEE, (2015)Using Interval Constraint Solving Techniques to better understand and predict future behaviors of dynamic problems., and . NAFIPS, page 1-6. IEEE, (2016)Why Sparse? Fuzzy Techniques Explain Empirical Efficiency of Sparsity-Based Data- and Image-Processing Algorithms., , , and . WCSC, volume 361 of Studies in Fuzziness and Soft Computing, page 419-428. Springer, (2016)Towards predictions of large dynamic systems' behavior using reduced-order modeling and interval computations., , , , and . SMC, page 345-350. IEEE, (2017)Downscaling Satellite Soil Moisture Using a Modular Spatial Inference Framework., , , , and . Remote. Sens., 14 (13): 3137 (2022)Model reduction: Why it is possible and how it can potentially help to control swarms of Unmanned Arial Vehicles (UAVs)., , , and . NAFIPS/WConSC, page 1-6. IEEE, (2015)