Publications - Roland Pulch

Singh, A.K.; Mehra, M.; Pulch, R.: Non-local physics informed neural networks for forward and inverse problems containing non-local operators. Neural Computing and Applications (2024).

Kugelmann, B.; Pulch, R.: Optimal control of a dengue model with cross-immunity. J. Math. Ind. 14:8 (2024).

Pulch, R.; Sète, O.: Stochastic Galerkin method and port-Hamiltonian form for linear first-order ordinary differential equations. Int. J. Uncertain. Quantif. 14:4 (2024), 65-82.

Pulch, R.; Sète, O.: The Helmholtz equation with uncertainties in the wavenumber. J. Sci. Comput. 98:60 (2024).

Pulch, R.; Sandu, A.: Rosenbrock-W methods for stochastic Galerkin systems. J. Comput. Appl. Math. 438 (2024), 115527.

Pulch, R.: Stochastic Galerkin method and port-Hamiltonian form for linear dynamical systems of second order. Math. Comput. Simulat. 216 (2024), 187-197.

Pulch, R.: Image analysis for patterns in solutions of reaction-diffusion equations. J. Comput. Appl. Math. 414 (2022), 114429.

Pulch, R.; Narayan, A.; Stykel, T.: Sensitivity analysis of random linear differential-algebraic equations using system norms. J. Comput. Appl. Math. 397 (2021), 113666.

Youssef, M.; Pulch, R.: Poly-sinc solution of stochastic elliptic differential equations. J. Sci. Comput. 87:82 (2021).

Pulch, R.; Narayan, A.: Sensitivity analysis of random linear dynamical systems using quadratic outputs. J. Comput. Appl. Math. 387 (2021), 112491.

Pulch, R.: Frequency domain integrals for stability preservation in Galerkin-type projection-based model order reduction. Int. J. Control 94:7 (2021), 1734-1750.

Pulch, R.; Youssef, M.: Machine learning for trajectories of parametric nonlinear dynamical systems. Journal of Machine Learning for Modeling and Computing 1:1 (2020), 75-95.

Pulch, R.: Stability-preserving model order reduction for linear stochastic Galerkin systems. J. Math. Ind. 9:10 (2019).

Pulch, R.; Narayan, A.: Balanced truncation for model order reduction of linear dynamical systems with quadratic outputs. SIAM J. Sci. Comput. 41:4 (2019), A2270-A2295.

Pulch, R.; Augustin, F.: Stability preservation in stochastic Galerkin projections of dynamical systems. SIAM/ASA J. Uncertainty Quantification 7:2 (2019), 634-651.

Pulch, R.: Stability preservation in Galerkin-type projection-based model order reduction. Numer. Algebra Contr. Optim. 9:1 (2019), 23-44.

Pulch, R.: Model order reduction for random nonlinear dynamical systems and low-dimensional representations for their quantities of interest. Math. Comput. Simulat. 166 (2019), 76-92.

Putek, P.; Janssen, R.; Niehof, J.; ter Maten, E.J.W.; Pulch, R.; Tasić, B.; Günther, M.: Nanoelectronic COupled Problems Solutions: uncertainty quantification for analysis and optimization of an RFIC interference problem. J. Math. Ind. 8:12 (2018).

Jakeman, J.D.; Pulch, R.: Time and frequency domain methods for basis selection in random linear dynamical systems. Int. J. Uncertain. Quantif. 8:6 (2018), 495-510.

Pulch, R.; Estévez Schwarz, D.; Lamour, R.: Index-analysis for a method of lines discretising multirate partial differential algebraic equations. Appl. Numer. Math. 130 (2018), 51-69.

Pulch, R.: Model order reduction and low-dimensional representations for random linear dynamical systems. Math. Comput. Simulat. 144 (2018), 1-20.

Putek, P.; Paplicki, P.; Pulch, R.; ter Maten, E.J.W.; Günther, M.; Pałka, R.: Multi-objective topology optimization of a permanent magnet machine to reduce electromagnetic losses and cogging torque. Int. J. Appl. Electrom. 53:S2 (2017), S203-S212.

Pulch, R.: A Hankel norm for quadrature rules solving random linear dynamical systems. J. Comput. Appl. Math. 316 (2017), 307-318.

Soll, T.; Pulch, R.: Sample selection based on sensitivity analysis in parameterized model order reduction. J. Comput. Appl. Math. 316 (2017), 369-379.

Freitas, F.D.; Pulch, R.; Rommes, J.: Fast and accurate model reduction for spectral methods in uncertainty quantification. Int. J. Uncertain. Quantif. 6:3 (2016), 271-286.

ter Maten, E.J.W.; Putek, P.; Günther, M.; Pulch, R.; Tischendorf, C.; Strohm, C.; Schoenmaker, W.; Meuris, P.; De Smedt, B.; Benner, P.; Feng, L.; Banagaaya, N.; Yue, Y.; Janssen, R.; Dohmen, J.J.; Tasić, B.; Deleu, F.; Gillon, R.; Wieers, A.; Brachtendorf, H.G.; Bittner, K.; Kratochvíl, T.; Petřzela, J.; Sotner, R.; Gotthans, T.; Dřínovský, J.; Schöps, S.; Duque, D.J.; Casper, T.; De Gersem, H.; Römer, U.; Reynier, P.; Barroul, P.; Masliah, D.; Rousseau, B.: Nanoelectronic coupled problems solutions - nanoCOPS: modelling, multirate, model order reduction, uncertainty quantification, fast fault simulation. J. Math. Ind. 7:2 (2016).

Putek, P.; Pulch, R.; Bartel, A.; ter Maten, E.J.W.; Günther, M.; Gawrylcyzk, K.M.: Shape and topology optimization of a permanent-magnet machine under uncertainties. J. Math. Ind. 6:11 (2016).

Putek, P.; Meuris, P.; Pulch, R.; ter Maten, J.; Schoenmaker, W.; Günther, M.: Uncertainty quantification for robust topology optimization of power transistor devices. IEEE Trans. Magn. 52:3 (2016), #1700104.

Mohaghegh, K.; Pulch, R.; ter Maten, J.: Model order reduction using singularly perturbed systems. Appl. Numer. Math. 103 (2016), 72-87.

Kugelmann, B.; Pulch, R.: Existence and uniqueness of optimal solutions for multirate partial differential algebraic equations. Appl. Numer. Math. 97 (2015), 69-87.

Pulch, R.; ter Maten, J.: Stochastic Galerkin methods and model order reduction for linear dynamical systems. Int. J. Uncertain. Quantif. 5:3 (2015), 255-273.

Pulch, R.; ter Maten, J.; Augustin, F.: Sensitivity analysis and model order reduction for random linear dynamical systems. Math. Comput. Simulat. 111 (2015), 80-95.

Bartel, A.; Hülsmann, T.; Kühn, J.; Pulch, R.; Schöps, S.: Influence of measurement errors on transformer inrush currents using different material models. IEEE Trans. Magn. 50:2 (2014), 485-488.

Di Bucchianico, A.; ter Maten, J.; Pulch, R.; Janssen, R.; Niehof, J.; Hanssen, J.; Kapora, S.: Robust and efficient uncertainty quantification and validation of RFIC isolation. Radioengineering 23:1 (2014), 308-318.

Pulch, R.: Stochastic collocation and stochastic Galerkin methods for linear differential algebraic equations. J. Comput. Appl. Math. 262 (2014), 281-291.

Pulch, R.: Stochastic Galerkin methods for analyzing equilibria of random dynamical systems. SIAM/ASA J. Uncertainty Quantification 1:1 (2013), 408-430.

Pulch, R.: Polynomial chaos for semi-explicit differential algebraic equations of index 1. Int. J. Uncertain. Quantif. 3:1 (2013), 1-23.

Pulch, R.: Polynomial chaos for boundary value problems of dynamical systems. Appl. Numer. Math. 62:10 (2012), 1477-1490.

Pulch, R.; Xiu, D.: Generalised polynomial chaos for a class of linear conservation laws. J. Sci. Comput. 51:2 (2012), 293-312.

Pulch, R.: Polynomial chaos for linear differential algebraic equations with random parameters. Int. J. Uncertain. Quantif. 1:3 (2011), 223-240.

Pulch, R.: Modelling and simulation of autonomous oscillators with random parameters. Math. Comput. Simulat. 81:6 (2011), 1128-1143.

Pulch, R.; van Emmerich, C.: Polynomial chaos for simulating random volatilities. Math. Comput. Simulat. 80:2 (2009), 245-255.

Pulch, R.: Polynomial chaos for multirate partial differential algebraic equations with random parameters. Appl. Numer. Math. 59:10 (2009), 2610-2624.

Bartel, A.; Knorr, S.; Pulch, R.: Wavelet-based adaptive grids for multirate partial differential-algebraic equations. Appl. Numer. Math. 59:3-4 (2009), 495-506.

Pulch, R.: Initial-boundary value problems of warped MPDAEs including minimisation criteria. Math. Comput. Simulat. 79:2 (2008), 117-132.

Pulch, R.: Variational methods for solving warped multirate partial differential algebraic equations. SIAM J. Sci. Comput. 31:2 (2008), 1016-1034.

Pulch, R.; Günther, M.; Knorr, S.: Multirate partial differential algebraic equations for simulating radio frequency signals. Euro. Jnl. of Applied Mathematics 18 (2007), 709-743.

Pulch, R.: Multidimensional models for analysing frequency modulated signals. Math. Comp. Modell. Dyn. Syst. 13:4 (2007), 315-330.

Pulch, R.: Multi time scale differential equations for simulating frequency modulated signals. Appl. Numer. Math. 53:2-4 (2005), 421-436.

Pulch, R.: Finite difference methods for multi time scale differential algebraic equations. Z. Angew. Math. Mech. 83:9 (2003), 571-583.

Pulch, R.; Günther, M.: A method of characteristics for solving multirate partial differential equations in radio frequency application. Appl. Numer. Math. 42:1 (2002), 397-409.

Pulch, R.: Stochastic Galerkin method for linear port-Hamiltonian differential-algebraic equations. to appear in: Scientific Computing in Electrical Engineering SCEE 2024. Mathematics in Industry, Springer, 2024.

Pulch, R.; Kugelmann, B.: Optimal control of a dengue fever model with delay. to appear in: Progress in Industrial Mathematics at ECMI 2023. Mathematics in Industry, Springer 2024.

Pulch, R.: W-schemes in spectral methods for reaction-diffusion equations. Proc. Appl. Math. Mech. (2024), e202400063.

Pulch, R.: Sensitivity analysis of random linear dynamical models using system norms. in: van Beurden, M.; Budko, N.V.; Ciuprina, G.; Schilders, W.; Bansal, H.; Barbulescu, R. (eds.): Scientific Computing in Electrical Engineering SCEE 2022. Mathematics in Industry 43, Springer, Switzerland 2024, pp. 208-216.

Pulch, R.: Energy-based model order reduction for linear stochastic Galerkin systems of second order. Proc. Appl. Math. Mech. 23 (2023), e202300038.

Pulch, R.: Delay differential equations for epidemic models with temporary immunity. in: Ehrhardt, M.; Günther, M. (eds.): Progress in Industrial Mathematics at ECMI 2021. Mathematics in Industry Vol. 39, Springer, Switzerland 2022, pp. 99-106.

Pulch, R.; Youssef, M.: Machine learning for initial value problems of parameter-dependent dynamical systems. in: van Beurden, M.; Budko, N.V.; Schilders, W. (eds.): Scientific Computing in Electrical Engineering SCEE 2020, Mathematics in Industry Vol. 36, Springer International Publishing 2022, pp. 231-239.

Pulch, R.: A machine learning approach for identifying an effective dimension in parametric dynamical systems. Proc. Appl. Math. Mech. 21 (2021), e202100252.

Pulch, R.: Stability preservation in model order reduction of linear dynamical systems. in: Nicosia, G.; Romano, V. (eds.): Scientific Computing in Electrical Engineering SCEE 2018, Mathematics in Industry Vol. 32, Springer, Switzerland 2020, pp. 277-285.

Pulch, R.; Schöps, S.: Sparse representations for uncertainty quantification of a coupled field-circuit problem. in: Faragó, I.; Izsák, F.; Simon, P. (eds.): Progress in Industrial Mathematics at ECMI 2018. Mathematics in Industry Vol. 30, Springer, Berlin 2019, pp. 11-18.

Pulch, R.; Narayan, A.: An error bound for the standard deviation in model order reduction of linear stochastic Galerkin systems. Proc. Appl. Math. Mech. 19 (2019), e201900028.

Pulch, R.: Solving high-dimensional Lyapunov inequalities to obtain linear port-Hamiltonian systems. Proc. Appl. Math. Mech. 18 (2018), e201800205.

Kugelmann, B.; Pulch, R.: Robust optimal control of fishing in a three competing species model. IFAC-PapersOnLine 51:2 (2018), pp. 7-12.

Liu, Q.; Pulch, R.: Numerical methods for derivative-based global sensitivity analysis in high dimensions. in: Langer. U.; Amrhein, W.; Zulehner, W. (eds.): Scientific Computing in Electrical Engineering SCEE 2016, Mathematics in Industry Vol. 28, Springer, Berlin 2018, pp. 157-167.

Putek, P.; Janssen, R.; Niehof, J.; ter Maten, E.J.W.; Pulch, R.; Günther, M.; Tasić, B.: Robust optimization of an RFIC isolation problem under uncertainties. in: Langer. U.; Amrhein, W.; Zulehner, W. (eds.): Scientific Computing in Electrical Engineering SCEE 2016, Mathematics in Industry Vol. 28, Springer, Berlin 2018, pp. 177-186.

Pulch, R.: Quadrature methods and model order reduction for sparse approximations in random linear dynamical systems. in: Langer. U.; Amrhein, W.; Zulehner, W.(eds.): Scientific Computing in Electrical Engineering SCEE 2016, Mathematics in Industry Vol. 28, Springer, Berlin 2018, pp. 203-217.

Pulch, R.; Putek, P.; De Gersem, H.; Gillon, R.: Identification of probabilistic input data for a glue-die-package problem. in: Quintela, P.; Barral, P.; Gómez, D.; Pena, F.J.;Rodríguez, J.; Salgado, P.; Vázquez-Mendéz, M.E. (eds.): Progress in Industrial Mathematics at ECMI 2016. Mathematics in Industry Vol. 26, Springer, 2018, pp. 255-262.

Putek, P.; Janssen, R.; Niehof, J.; ter Maten, E.J.W.; Pulch, R.; Tasić, B.; Günther, M.: Nanoelectronic Coupled Problems Solutions: Uncertainty quantification of RFIC interference. in: Quintela, P.; Barral, P.; Gómez, D.; Pena, F.J.;Rodríguez, J.; Salgado, P.; Vázquez-Mendéz, M.E. (eds.): Progress in Industrial Mathematics at ECMI 2016. Mathematics in Industry Vol. 26, Springer, 2018, pp. 271-279.

Soll, T.; Pulch, R.: Parameterized model order reduction by superposition of locally reduced bases. in: Quintela, P.; Barral, P.; Gómez, D.; Pena, F.J.;Rodríguez, J.; Salgado, P.; Vázquez-Mendéz, M.E. (eds.): Progress in Industrial Mathematics at ECMI 2016. Mathematics in Industry Vol. 26, Springer, 2018, pp. 737-744.

Pulch, R.: A Hankel-type norm of a random mass-spring-damper system. Proc. Appl. Math. Mech. 17 (2017), pp. 729-730.

Pulch, R.: Input-output behaviour of a stochastic Galerkin method for a low pass filter. Proc. Appl. Math. Mech. 16 (2016), pp. 677-678.

Tasić, B.; Dohmen, J.J.; Janssen, R.; ter Maten, E.J.W.; Beelen, T.G.J.; Pulch, R.: Fast time-domain simulation for reliable fault detection. in: Proceedings DATE-2016, Design, Automation and Test in Europe, March 14-18, 2016, Dresden, Germany, pp. 301-306.

Putek, P.; Meuris, P.; Pulch, R.; ter Maten, E.J.W.; Günther, M.; Schoenmaker, W.; Deleu, F.; Wieers, A.: Shape optimization of a power MOS device under uncertainties. in: Proceedings DATE-2016, Design, Automation and Test in Europe, March 14-18, 2016, Dresden, Germany, pp. 319-324.

Pulch, R.: Model order reduction for stochastic expansions of electric circuits. in: Bartel, A.; Clemens, M.; Günther, M.; ter Maten, E.J.W. (eds.): Scientific Computing in Electrical Engineering SCEE 2014. Mathematics in Industry Vol. 23, Springer, Berlin 2016, pp. 223-232.

Putek, P.; Gausling, K.; Bartel, A.; Gawrylczyk, K.M.; ter Maten, E.J.W.; Pulch, R.; Günther, M.: Robust topology optimization of a permanent magnet synchronous machine using multi-level set and stochastic collocation methods. in: Bartel, A.; Clemens, M.; Günther, M.; ter Maten, E.J.W. (eds.): Scientific Computing in Electrical Engineering SCEE 2014. Mathematics in Industry Vol. 23, Springer, Berlin 2016, pp. 233-242.

Liu, Q.; Petzold, T.; Nadolski, D.; Pulch, R.: Simulation of thermomechanical behavior subjected to induction hardening. in: Bartel, A.; Clemens, M.; Günther, M.; ter Maten, E.J.W. (eds.): Scientific Computing in Electrical Engineering SCEE 2014. Mathematics in Industry Vol. 23, Springer, Berlin 2016, pp. 133-142.

Pulch, R.; Bartel, A.; Schöps, S.: Quadrature methods with adjusted grids for stochastic models of coupled problems. in: Russo, G.; Capasso, V.; Nicosia, G.; Romano, V. (eds.): Progress in Industrial Mathematics at ECMI 2014. Mathematics in Industry Vol. 22, Springer, Berlin 2016, pp. 377-384.

Janssen, H.H.J.M.; Benner, P.; Bittner, K.; Brachtendorf, H.G.; Feng, L.; ter Maten, E.J.W.; Pulch, R.; Schoenmaker, W.; Schöps, S.; Tischendorf, C.: The European project nanoCOPS for nanoelectronic coupled problems solutions. in: Russo, G.; Capasso, V.; Nicosia, G.; Romano, V. (eds.): Progress in Industrial Mathematics at ECMI 2014. Mathematics in Industry Vol. 22, Springer, Berlin 2016, pp. 835-842.

Putek, P.; Meuris, P.;  Günther, M.; ter Maten, E.J.W.; Pulch, R.; Wieers, A.; Schoenmaker, W.: Uncertainty quantification in electro-thermal coupled problems based on a power transistor device. IFAC-PapersOnLine 48:1 (2015), pp. 938-939.

Pulch, R.; Kugelmann, B.: DAE-formulation for optimal solutions of a multirate model. Proc. Appl. Math. Mech. 15 (2015), pp. 615-616.

Janssen, R.; ter Maten, E.J.W.; Tischendorf, C.; Brachtendorf, H.G.; Bittner, K.; Schoenmaker, W.; Benner, P.; Feng, L.; Pulch, R.; Deleu, F.; Wieers, A.: The nanoCOPS project on algorithms for nanoelectronic coupled problems solutions. in: Schrefler, B.; Onate, E.; Papadrakakis, M. (eds.): Coupled Problems in Science and Engineering VI. San Servolo, Venice, Italy, 18-20 May 2015, CIMNE 2015, pp. 1029-1036.

Pulch, R.: Polynomial-chaos based methods for differential algebraic equations with random parameters. in: Fontes, M.; Günther, M.; Marheineke, N. (eds.): Progress in Industrial Mathematics at ECMI 2012. Mathematics in Industry Vol. 19, Springer 2014, pp. 355-360.

ter Maten, E.J.W.; Pulch, R.; Schilders, W.H.A.; Janssen, H.H.J.M.: Efficient calculation of uncertainty quantification. in: Fontes, M.; Günther, M.; Marheineke, N. (eds.): Progress in Industrial Mathematics at ECMI 2012. Mathematics in Industry Vol. 19, Springer 2014, pp. 361-370.

Pulch, R.; ter Maten, E.J.W.; Augustin, F.: Sensitivity analysis of linear dynamical systems in uncertainty quantification. Proc. Appl. Math. Mech. 13 (2013), pp. 507-508.

Pulch, R.: Modelling and simulation of forced oscillators with random periods. in: Michielsen, B.; Poirier, J.-R. (eds.): Scientific Computing in Electrical Engineering SCEE 2010. Mathematics in Industry Vol. 16, Springer, Berlin 2012, pp. 275-284.

Pulch, R.: Polynomial chaos for the computation of failure probabilities in periodic problems. in: Roos, J.; Costa, L. (eds.): Scientific Computing in Electrical Engineering SCEE 2008. Mathematics in Industry Vol. 14, Springer, Berlin 2010, pp. 191-198.

Alì, G.; Mascali, G.; Pulch, R.: Hyperbolic PDAEs for semiconductor devices coupled with circuits. in: Roos, J.; Costa, L. (eds.): Scientific Computing in Electrical Engineering SCEE 2008. Mathematics in Industry Vol. 14, Springer, Berlin 2010, pp. 305-312.

Mohaghegh, K.; Striebel, M.; ter Maten, E.J.W.; Pulch, R.: Nonlinear model order reduction based on trajectory piecewise linear approach: comparing different linear cores. in: Roos, J.; Costa, L. (eds.): Scientific Computing in Electrical Engineering SCEE 2008. Mathematics in Industry Vol. 14, Springer, Berlin 2010, pp. 563-570.

Pulch, R.: Polynomial chaos expansions for analysing oscillators with uncertainties. in: Troch, I.; Breitenecker, F. (eds.): Proceedings MATHMOD 09 Vienna (2009).

Mohaghegh, K.; Pulch, R.; Striebel, M.; ter Maten, E.J.W.: Model order reduction for semi-explicit systems of differential algebraic equations. in: Troch, I.; Breitenecker, F. (eds.): Proceedings MATHMOD 09 Vienna (2009).

Pulch, R.: Polynomial chaos for analysing periodic processes of differential algebraic equations with random parameters. Proc. Appl. Math. Mech. 8 (2008), pp. 10069-10072.

Pulch, R.: Multirate models for simulating a Colpitts oscillator. Proc. Appl. Math. Mech. 7 (2007), pp. 4050021-4050022.

Bartel, A.; Pulch, R.: A concept for classification of partial differential algebraic equations in nanoelectronics. in: Bonilla, L.L.; Moscoso, M.; Platero, G.; Vega, J.M. (eds.): Progress in Industrial Mathematics at ECMI 2006. Mathematics in Industry Vol. 12, Springer, Berlin 2007, pp. 506-511.

Greb, J.; Pulch, R.: Simulation of quasiperiodic signals via warped MPDAEs using Houben’s approach. in: Ciuprina, G.; Ioan, D. (eds.): Scientific Computing in Electrical Engineering SCEE 2006. Mathematics in Industry Vol. 11, Springer, Berlin 2007, pp. 237-243.

Voß, T.; Pulch, R.; ter Maten, E.J.W.; El Guennouni, A.: Trajectory piecewise linear approach for nonlinear differential-algebraic equations in circuit simulation. in: Ciuprina, G.; Ioan, D. (eds.): Scientific Computing in Electrical Engineering SCEE 2006. Mathematics in Industry Vol. 11, Springer, Berlin 2007, pp. 167-174.

Pulch, R.: Semidiscretisation methods for warped MPDAEs. in: Anile, A.M.; Alì, G.; Mascali, G. (eds.): Scientific Computing in Electrical Engineering SCEE 2004. Mathematics in Industry Vol. 9, Springer 2006, pp. 101-106.

Pulch, R.: Warped MPDAE models with continuous phase conditions. in: Di Bucchianico, A.; Mattheij, R.M.M.; Peletier, M.A. (eds.): Progress in Industrial Mathematics at ECMI 2004. Mathematics in Industry Vol. 8, Springer 2006, pp. 179-183.

Pulch, R.: Warped MPDAE models including minimisation criteria for the simulation of RF signals. Proc. Appl. Math. Mech. 5 (2005), pp. 811-814.

Pulch, R.: Numerical techniques for solving multirate partial differential algebraic equations. in: Schilders, W.H.A.; ter Maten, E.J.W.; Houben, S.H.M.J. (eds.): Scientific Computing in Electrical Engineering SCEE 2002. Mathematics in Industry Vol. 4, Springer 2004, pp. 337-344.

Pulch, R.: A parallel finite difference algorithm for multirate partial differential algebraic equations. in: Antreich, K.; Bulirsch, R.; Gilg, A.; Rentrop, P. (eds.): Modeling, Simulation and Optimization of Integrated Circuits. International Series of Numerical Mathematics Vol. 146, Birkhäuser 2003, pp. 153-166.

Bartel, A.; Günther, M.; Pulch, R.; Rentrop, P.: Numerical techniques for different time scales in electric circuit simulation. in: Breuer, M.; Durst, F.; Zenger, Ch. (eds.): High-Performance Scientific and Engineering Computing. Lecture Notes in Computational Science and Engineering, Springer 2002, pp. 343-360.

Pulch, R.: PDAE Methoden zur numerischen Simulation quasiperiodischer Grenzzyklen von Oszillatorschaltungen. Fortschritt-Berichte VDI, Reihe 20 Nr. 380, VDI-Verlag, Düsseldorf, 2004 (ISBN 3-18-3 38020-X).

ter Maten, E.J.W.; Brachtendorf, H.-G.; Pulch, R.; Schoenmaker, W.; De Gersem, H. (eds.): Nanoelectronic Coupled Problems Solutions. Mathematics in Industry Vol. 29, Springer International Publishing, 2019 (ISBN 978-3-030-30725-7).

Bittner, K.; Brachtendorf, H.G.; Pulch, R.: Challenges in the simulation of radio frequency circuits. in: Günther, M., Schilders, W. (eds.): Novel Mathematics Inspired by Industrial Challenges. Mathematics in Industry Vol. 38, Springer, Cham 2022, pp. 155-177.

Pulch, R.: Model order reduction in uncertainty quantification. in: Benner, P.; Grivet-Talocia, S.; Quarteroni, A.; Rozza, G.; Schilders, W.; Silveira, L.M. (eds.): Model Order Reduction Volume 3: Applications. De Gruyter, 2021, pp. 321-344.

Alì, G.; Culpo, M.; Pulch, R.; Romano, V.; Schöps, S.: PDAE Modeling and Discretization. in: Günther, M. (ed.): Coupled Multiscale Simulation and Optimization in Nanoelectronics. Mathematics in Industry Vol. 21, Springer, Berlin 2015, pp. 15-102.

Antoulas, A.C.; Ionutiu, R.; Martins, N.; ter Maten, E.J.W.; Mohaghegh, K.; Pulch, R.; Rommes, J.; Saadvandi, M.; Striebel, M.: Model Order Reduction: Methods, Concepts and Properties. in: Günther, M. (ed.): Coupled Multiscale Simulation and Optimization in Nanoelectronics. Mathematics in Industry Vol. 21, Springer, Berlin 2015, pp. 159-265.

Ciuprina, G.; Fernandez Villena, J.; Ioan, D.; Ilievski, Z.; Kula, S.; ter Maten, E.J.W.; Mohaghegh, K.; Pulch, R.; Schilders, W.H.A.; Silveira, L.M.; Stefanescu, A.; Striebel, M.: Parameterized Model Order Reduction. in: Günther, M. (ed.): Coupled Multiscale Simulation and Optimization in Nanoelectronics. Mathematics in Industry Vol. 21, Springer, Berlin 2015, pp. 267-359.

Pulch, R.: Transformation qualities of warped multirate partial differential algebraic equations. in: Breitner, M.; Denk, G.; Rentrop, P. (eds.): From Nano to Space - Applied Mathematics Inspired by Roland Bulirsch. Springer, Berlin 2008, pp. 27-42.

Knorr, S.; Pulch, R.; Günther, M.: Wavelet-collocation of multirate PDAEs for the simulation of radio frequency circuits. in: Jäger, W.; Krebs, H.-J. (eds.): Mathematics - Key Technology for the Future - Joint Projects between Universities and Industry 2004-2007. Springer, Berlin 2008, pp. 19-28.

Konermann, S.; Günther, M.; Pulch, R.; Bartel, A.; Stein, M.: Method of Measuring the Thickness Profile of a Film Tube – EP2128563. submitted 2008-05-30, published 2009-02-12, withdrawn 2013-12-03.