Publications

Navid Ghajarnia, Mahdi Akbari, Peyman Saemian, Mohammad Reza Ehsani, Seyed-Mohammad Hosseini-Moghari, Asghar Azizian, Zahra Kalantari, Ali Behrangi, Mohammad J. Tourian, Björn Klöve, and Ali Torabi Haghighi. Evaluating the Evolution of ECMWF Precipitation Products Using Observational Data for Iran: From ERA40 to ERA5. Earth and Space Science, (9)10:e2022EA002352, 2022. [PUMA: ERA, analysis decomposition, error estimates, evaluation, geoinst imported multi-scale precipitation statistical] URL

Daniel Wirtz, and Bernard Haasdonk. Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems. Systems & Control Letters, (61)1:203--211, Elsevier BV, 2012. [PUMA: subspace error dynamical kernel a-posteriori from:britsteiner methods, ians nonlinear systems, offline/online decomposition, projection estimates, model anm reduction,] URL

I. Martini, G. Rozza, and Bernard Haasdonk. Reduced basis approximation and a-posteriori error estimation for the coupled Stokes-Darcy system. Advances in Computational Mathematics, (41)5:1131--1157, 2015. [PUMA: Error decomposition; medium 76D07 from:britsteiner Porous Reduced ians method; basis problem; Stokes 76S05; estimation; equation; Domain Non-coercive flow; anm 65N55;] URL

Daniel Wirtz, and Bernard Haasdonk. A-posteriori error estimation for parameterized kernel-based systems. Proc. MATHMOD 2012 - 7th Vienna International Conference on Mathematical Modelling, 2012. [PUMA: a-posteriori anm decomposition, dynamical error estimates, ians kernel methods, model nonlinear offline/online parameterized projection reduction, subspace systems,] URL

Daniel Wirtz, and Bernard Haasdonk. Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems. Systems & Control Letters, (61)1:203--211, Elsevier BV, 2012. [PUMA: a-posteriori anm decomposition, dynamical error estimates, ians kernel methods, model nonlinear offline/online projection reduction, subspace systems,] URL

I. Martini, G. Rozza, and Bernard Haasdonk. Reduced basis approximation and a-posteriori error estimation for the coupled Stokes-Darcy system. Advances in Computational Mathematics, (41)5:1131--1157, 2015. [PUMA: 65N55; 76D07 76S05; Domain Error Non-coercive Porous Reduced Stokes anm basis decomposition; equation; estimation; flow; ians medium method; problem;] URL

J. Antes, and I. Kallfass. Performance Estimation for Broadband Multi-Gigabit Millimeter and Sub-Millimeter Wave Wireless Communication Links. IEEE Trans. on Microwave Theory and Techniques, (63)10:3288--3299, 2015. [PUMA: (BER);complex (EVM);millimeter-wave (MMICs);performance (SER);wireless (mmw) amplitude bandwidths;in-phase broadband channels;E-band characteristics;performance circuits communication communication;Bit communication;mmw error error;submillimeter-wave estimation;performance estimation;symbol factor;local frequency frontend frontend;broadband imbalance;in-phase imbalance;limiting imbalance;quadrature imperfection;performance integrated keying keying;radio limiting limits;quadrature links;broadband links;channel magnitude millimeter-wave modulated modulation modulation;error monolithic multigigabit networks;error networks;submillimetre noise;Wireless noise;extremely noise;nonideal noise;quadrature oscillator phase phase-shift range;symbol rate rate;Estimation;Modulation;Phase rates;wireless shift signals;relative statistics;millimetre submillimeter-wave systems;Bandwidth;Bit vector waves;phase waves;wireless wireless]

S. Dörner, S. Cammerer, J. Hoydis, and S. ten Brink. Deep Learning Based Communication Over the Air. IEEE Journal of Selected Topics in Signal Processing, (12)1:132-143, February 2018. [PUMA: myown network;over-the-air;software-defined software-defined from:sdnr transmissions;open-source (artificial systems;block-based module;transmitter transmission;receiver implementations;deep computing;two-step nets;radio intelligence);neural libraries;continuous learning;communications learning;end-to-end rate;over-the-air synchronization;frame error transmissions;receiver deep neural software synchronization data learning;modulation;neural learning libraries;software radio implementations;off-the-shelf networks;NNs;block procedure;end-to-end radio;synchronisation;telecommunication radios;Training;Receivers;Communication networks;Hardware;Transmitters;Synchronization;Autoencoder;communication;deep receivers;software systems;Artificial]

Daniel Groß, Heiner Früh, Daniel Contreras, Krzysztof Rudion, Linda Rupp, and Christian Lakenbrink. Evaluation of a Three-Phase Distribution System State Estimation for Operational Use in a Real Medium Voltage Grid. 2019. [PUMA: data distribution error estimation measurement measurements medium meter placement pseudo real state system three-phase voltage] URL

D. Wirtz, and B. Haasdonk. Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems. Systems and Control Letters, (61)1:203 - 211, 2012. [PUMA: a-posteriori decomposition, dynamical error estimates, from:mhartmann ians kernel methods, model nonlinear offline/online projection reduction, subspace systems, vorlaeufig] URL

F. D. Gaspoz, P. Morin, and A. Veeser. A posteriori error estimates with point sources in fractional sobolev spaces. Numerical Methods for Partial Differential Equations, (33)4:1018--1042, 2017. [PUMA: Dirac Sobolev a adaptivity, element error estimators, finite fractional from:mhartmann ians mass, methods, posteriori spaces vorlaeufig] URL

F. D. Gaspoz, C. Kreuzer, K. Siebert, and D. Ziegler. A convergent time-space adaptive $dG(s)$ finite element method for parabolic problems motivated by equal error distribution. Submitted, 2017. [PUMA: a adaptivity, convergence, element equation error estimators, finite from:mhartmann heat ians methods, posteriori vorlaeufig] URL

I. Martini, G. Rozza, and B. Haasdonk. Reduced basis approximation and a-posteriori error estimation for the coupled Stokes-Darcy system. Advances in Computational Mathematics, (41)5:1131--1157, 2015. [PUMA: 65N55; 76D07 76S05; Domain Error Non-coercive Porous Reduced Stokes basis decomposition; equation; estimation; flow; from:mhartmann ians medium method; problem; vorlaeufig] URL

J. Manuel Cascón, Ricardo H. Nochetto, and Kunibert G. Siebert. Design and Convergence of AFEM in $H(div)$. Mathematical Models & Methods in Applied Sciences, (17)11:1849--1881, 2007. [PUMA: A convergence; error estimate; from:mhartmann ians multigrid oscillation; posteriori preconditioning reduction; vorlaeufig] URL

Daniel Wirtz, and Bernard Haasdonk. A-posteriori error estimation for parameterized kernel-based systems. Proc. MATHMOD 2012 - 7th Vienna International Conference on Mathematical Modelling, 2012. [PUMA: a-posteriori decomposition, dynamical error estimates, from:mhartmann ians kernel methods, model nonlinear offline/online parameterized projection reduction, subspace systems, vorlaeufig] URL

Fernando D. Gaspoz, and Pedro Morin. Convergence rates for adaptive finite elements. IMA J. Numer. Anal., (29)4:917--936, Oxford University Press, 2009. [PUMA: a adaptive elliptic equations error estimator, from:mhartmann ians mesh posteriori refinement, vorlaeufig]

F. D. Gaspoz, P. Morin, and A. Veeser. A posteriori error estimates with point sources in fractional sobolev spaces. Numerical Methods for Partial Differential Equations, (33)4:1018--1042, 2017. [PUMA: Dirac Sobolev a adaptivity, element error estimators, finite fractional mass, methods, posteriori spaces vorlaeufig] URL

D. Wirtz, and B. Haasdonk. Efficient a-posteriori error estimation for nonlinear kernel-based reduced systems. Systems and Control Letters, (61)1:203 - 211, 2012. [PUMA: a-posteriori decomposition, dynamical error estimates, kernel methods, model nonlinear offline/online projection reduction, subspace systems, vorlaeufig] URL

F. D. Gaspoz, C. Kreuzer, K. Siebert, and D. Ziegler. A convergent time-space adaptive $dG(s)$ finite element method for parabolic problems motivated by equal error distribution. Submitted, 2017. [PUMA: a adaptivity, convergence, element equation error estimators, finite heat methods, posteriori vorlaeufig] URL

I. Martini, G. Rozza, and B. Haasdonk. Reduced basis approximation and a-posteriori error estimation for the coupled Stokes-Darcy system. Advances in Computational Mathematics, (41)5:1131--1157, 2015. [PUMA: 65N55; 76D07 76S05; Domain Error Non-coercive Porous Reduced Stokes basis decomposition; equation; estimation; flow; medium method; problem; vorlaeufig] URL