Monday, September 19, 2022

Time Event (+)
12:45 - 13:30 Registration - Welcome of participants (H 3005)  
13:30 - 14:00 Opening (H 1012)  
14:00 - 14:45 Plenary (H 1012) - Chair: Mario Ohlberger (+)  
14:00 - 14:45 › Nonlinear balanced truncation: Scalable computation and manifold reduction - Boris Kramer, University of California, San Diego  
14:50 - 16:05 Session 1 (H 1012) - Chair: Mario Ohlberger (+)  
14:50 - 15:15 › Probabilistic reduced basis method for parameter-dependent problems - Marie BILLAUD FRIESS, Laboratoire de Mathématiques Jean Leray  
15:15 - 15:40 › Stability Analysis of Reduced Basis Model Predictive Control for Parametrized Optimal Control Problems - Saskia Dietze, RWTH Aachen University  
15:40 - 16:05 › Solving parametric PDEs with an enhanced model reduction method based on Linear/Ridge expansions - Constantin Greif, Ulm University  
16:05 - 16:30 Coffee break (H 3005/3006)  
16:30 - 17:45 Parallel Session 1a (H 1012) - Chair: Volker Mehrmann (+)  
16:30 - 16:55 › Data-driven learning of coarse basis functions in adaptive FETI-DP - Axel Klawonn, Department of Mathematics and Computer Science, Center for Data and Simulation Science  
16:55 - 17:20 › Data-driven model order reduction with the p-AAA algorithm - Linus Balicki, Department of Mathematics, Virginia Polytechnic Institute and State University  
17:20 - 17:45 › Data-driven reduced-order modeling of thermo-mechanical models of machine tools - Quirin Aumann, Chemnitz University of Technology / Technische Universität Chemnitz  
16:30 - 17:45 Parallel Session 1b (H 1058) - Chair: Lihong Feng (+)  
16:30 - 16:55 › Stochastic reduced order models for Bayesian estimation problems in fluid mechanics - valentin resseguier, Optimisation des procédés en Agriculture, Agroalimentaire et Environnement, Lab SCALIAN  
16:55 - 17:20 › A Reduced Basis Ensemble Kalman Method - Francesco Attilio Bruno Silva, Eindhoven University of Technology  
17:20 - 17:45 › Adapting Reduced Models for Importance Sampling - Patrick Héas, Institut National de Recherche en Informatique et en Automatique  
17:45 - 19:00 Parallel Session 2a (H 1012) - Chair: Volker Mehrmann (+)  
17:45 - 18:10 › Data-Driven Enhanced Model Reduction for Bifurcating Models in Computational Fluid Dynamics - Martin Hess, SISSA MathLab [Trieste]  
18:10 - 18:35 › Data driven reduced modelling of the Vlasov-Poisson equation - Guillaume Steimer, Inria Nancy - Grand Est, Institut de Recherche Mathématique Avancée  
17:45 - 19:00 Parallel Session 2b (H 1058) - Chair: Lihong Feng (+)  
17:45 - 18:10 › Parametric dynamic mode decomposition for nonlinear parametric dynamical systems - Shuwen Sun, Max Planck Institute for Dynamics of Complex Technical Systems  
18:10 - 18:35 › Dominant Subspaces of High-Fidelity Nonlinear Structured Parametric Dynamical Systems and Model Reduction - Igor Pontes Duff, Max Planck Institute for Dynamics of Complex Technical Systems  
18:35 - 19:00 › A greedy MOR method for the tracking of eigensolutions to parametric PDEs - Francesca Bonizzoni, Faculty of Mathematics, University of Augsburg  
19:30 - 21:30 Welcome Reception (A Foyer)  

Tuesday, September 20, 2022

Time Event (+)
08:30 - 09:15 Plenary (H 1012) - Chair: Peter Benner (+)  
08:30 - 09:15 › Randomization techniques for solving large scale linear algebra problems - Laura Grigori, INRIA Paris  
09:20 - 10:35 Session 2 (H 1012) - Chair: Peter Benner (+)  
09:20 - 09:45 › Model order reduction for Friedrichs' systems: a bridge between elliptic and hyperbolic problems - Davide Torlo, Scuola Internazionale Superiore di Studi Avanzati / International School for Advanced Studies  
09:45 - 10:10 › Nonlinear model order reduction for hyperbolic conservation laws by means of diffeomorphic transformations of space-time domains - Hendrik Kleikamp, Institute for Analysis and Numerics, University of Münster  
10:10 - 10:35 › Adaptive POD-DEIM correction for Turing pattern approximation in reaction-diffusion PDE systems - Angela Monti, Università del Salento  
10:35 - 11:00 Coffee break (H 3005/3006)  
11:00 - 12:40 Parallel Session 3a (H 1012) - Chair: Bernard Haasdonk (+)  
11:00 - 11:25 › A structure-preserving DEIM formulation for non-linearly stable hROMs of the incompressible Navier-Stokes equations - Robin Klein, Process and Energy Laboratory [Delft], Centrum Wiskunde & Informatica  
11:25 - 11:50 › Model Reduction of Navier-Stokes Equations using the Loewner Framework - Matthias Heinkenschloss  
11:50 - 12:15 › Model order reduction for turbulent and compressible flows: hybrid approaches in physics and geometry parametrization - giovanni stabile, International School for Advanced Studies, mathematics area, mathLab  
12:15 - 12:40 › POD-Based Adaptive Model Reduction to Accelerate Computational Fluid Dynamics - Victor Zucatti, Matthew J. Zahr  
11:00 - 12:40 Parallel Session 3b (H 1058) - Chair: Heike Faßbender (+)  
11:00 - 11:25 › Matrix-free Transfer Function Prediction using Model Reduction and Machine Learning - Lihong Feng, Max Planck Institute for Dynamics of Complex Technical Systems - Giulio Antonini, UAq EMC Laboratory, Department of Industrial and Information Engineering and Economics, University of L'Aquila  
11:25 - 11:50 › Deep-Reinforcement-Learning-informed Adaptive Refinement for High-order Discontinuous Galerkin Methods - Corbin Foucart, Massachusetts Institute of Technology  
11:50 - 12:15 › On error estimates for reduced models obtained by balanced truncation - Björn Liljegren-Sailer, Trier University  
12:15 - 12:40 › Automated model reduction for pharmacology models - Jorge Cisneros, University of Washington  
12:40 - 14:00 Lunch  
14:00 - 15:15 Session 3 (EW201) - Chair: Michael Hinze (+)  
14:00 - 14:25 › Model reduction for dynamics on deformable complex surfaces - Shaimaa Monem Abdelhafez, Max Planck Institute for Dynamics of Complex Technical Systems - Peter Benner, Max Planck Institute for Dynamics of Complex Technical Systems  
14:25 - 14:50 › Nonlinear Model Order Reduction for Three-dimensional Discretized FE Models using Graph Convolutional Autoencoders - Jörg Fehr, Institute of Engineering and Computational Mechanics, University of Stuttgart - Jonas Kneifl, Institute of Engineering and Computational Mechanics, University of Stuttgart  
14:50 - 15:15 › Nonlinear manifold Reduced Order Models with Convolutional Autoencoders and Reduced Over-Collocation method - Francesco Romor, SISSA MathLab [Trieste]  
15:20 - 16:05 Plenary (EW201) - Chair: Michael Hinze (+)  
15:20 - 16:05 › Learning to Forecast Dynamical Systems from Streaming Data - Rachel Ward, UT Austin  
16:05 - 16:30 Coffee break (H 3005/3006)  
16:30 - 17:45 Parallel Session 4a (H 1012) - Chair: Matthias Heinkenschloss (+)  
16:30 - 16:55 › Non-intrusive multi-physics PGD-based reduced model for the modeling of power electronic modules - Louis Schuler, Laboratoire de Mécanique Paris-Saclay, Mitsubishi Electric R&D Centre Europe [France]  
16:55 - 17:20 › Full Order Model and Reduced Order Model Consistency for Evolve-Filter-Relax Regularization - Maria Strazzullo, Department of Mathematical Sciences [Torino]  
17:20 - 17:45 › Hyper-reduction of geometrically parameterized nonlinear microstructures - Theron Guo, Eindhoven University of Technology  
16:30 - 17:45 Parallel Session 4b (H 1058) - Chair: Axel Klawonn (+)  
16:30 - 16:55 › Two-Scale Reduction of LOD Multiscale Models - Stephan Rave, University of Münster  
16:55 - 17:20 › Adaptive Localized Reduced Basis Methods in Multiscale PDE-Constrained Parameter Optimization - Tim Keil, Mathematics Münster, University of Münster  
17:20 - 17:45 › Model reduction of convection-dominated partial differential equations via optimization-based implicit feature tracking - Matthew Zahr, University of Notre Dame  

Wednesday, September 21, 2022

Time Event (+)
08:30 - 09:15 Plenary (H 1012) - Chair: Gianluigi Rozza (+)  
08:30 - 09:15 › Simulation-based Bayesian inference and surrogate modeling - Youssef Marzouk, Massachusetts Institute of Technology  
09:20 - 10:35 Session 4 (H 1012) - Chair: Gianluigi Rozza (+)  
09:20 - 09:45 › Deep learning and the dynamical low-rank approximation - Aaron Charous, Massachusetts Institute Of Technology  
09:45 - 10:10 › Generalized Neural Closure Models with Interpretability - Abhinav Gupta, Department of Mechanical Engineering [Massachusetts Institute of Technology]  
10:10 - 10:35 › Conditional gradient-based Identification of Non-linear Dynamics - Martin Weiser, Zuse Institute Berlin  
10:35 - 11:00 Coffee break (H 3005/3006)  
11:00 - 12:40 Parallel Session 5a (H 1012) - Chair: Anthony Nouy (+)  
11:00 - 11:25 › Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds - Silke Glas, University of Twente  
11:25 - 11:50 › Model reduction for port-Hamiltonian descriptor systems - Volker Mehrmann, Technische Universität Berlin  
11:50 - 12:15 › Dynamic Mode Decomposition for Continuous Port-Hamiltonian Systems - Jonas Nicodemus, Stuttgart Center for Simulation Science (SC SimTech), University of Stuttgart  
12:15 - 12:40 › A non-intrusive algorithm for parameterized model order reduction of LTI systems with guaranteed dissipativity - Tommaso Bradde, Politecnico di Torino, Department of Electronics and Telecommunications  
11:00 - 12:40 Parallel Session 5b (H 1058) - Chair: Boris Kramer (+)  
11:00 - 11:25 › Model reduction of descriptor systems with quadratic output functional - Jennifer Przybilla, Max Planck Institute for Dynamics of Complex Technical Systems  
11:25 - 11:50 › Learning Quadratic Embeddings for Nonlinear Dynamical Systems using Deep Learning - Pawan Goyal, Max Planck Institute for Dynamics of Complex Technical Systems  
11:50 - 12:15 › Impact of the Convergence of Series Expansions on Model Reduction of Quadratic-Bilinear Systems - Alejandro Diaz, Rice University  
12:15 - 12:40 › Data-Driven Model Reduction for Gas Network Digital Twins - Christian Himpe, University of Munster  
12:40 - 14:00 Lunch  
14:00 - 14:45 Plenary (H 1012) - Chair: Tobias Breiten (+)  
14:00 - 14:45 › Structure-preserving and adaptive reduced order models of conservative dynamical systems - Cecilia Pagliantini, TU/e Eindhoven  
15:00 - 16:30 Poster Blitz (H 1012) - Chair: Tobias Breiten (+)  
15:02 - 15:04 › Neural Closure Model for Dynamic Mode Decomposition Forecasts - Tony Ryu, Massachusetts Institute of Technology  
15:04 - 15:06 › A Differential Geometric Formulation for Model Order Reduction on Manifolds - Patrick Buchfink, University of Stuttgart  
15:06 - 15:08 › A machine learning-based reduced order model for the investigation of the haemodynamics in coronary artery bypass grafts - Pierfrancesco Siena, SISSA  
15:08 - 15:10 › Adaptive Gaussian Process Regression for Efficient Building of Surrogate Models in Inverse Problems - Phillip Semler, Zuse Institut Berlin  
15:10 - 15:12 › An efficient computational framework for atmospheric and ocean flows - Michele Girfoglio, Scuola Internazionale Superiore di Studi Avanzati / International School for Advanced Studies  
15:12 - 15:14 › Analysis of Hyper Reduction for the Computation of Nonlinear Normal Modes - Lukas Bürger, KULeuven  
15:14 - 15:16 › Balancing-related model reduction of large-scale sparse systems in MATLAB and Octave with the MORLAB toolbox - Steffen W. R. Werner, Courant Institute of Mathematical Sciences, New York University  
15:16 - 15:18 › Bayesian multi-fidelity inverse analysis for computationally demanding models in high stochastic dimensions - Jonas Nitzler, Institute for Computational Mechanics, Professorship of Data-driven Materials Modeling, Technical University of Munich  
15:18 - 15:20 › Combining adaptive model order reduction and stochastic collocation for uncertainty quantification of vibroacoustic systems - Ulrich Römer, Institut für Dynamik und Schwingungen, Technische Universität Braunschweig  
15:20 - 15:22 › Data enhanced reduced order methods for turbulent flows - Anna Ivagnes, SISSA MathLab [Trieste]  
15:22 - 15:24 › Data-driven approaches for system identication and reduction - Dimitrios Karachalios, Max Planck Institute for Dynamics of Complex Technical Systems  
15:24 - 15:26 › Data-Driven Linearization of Nonlinear Finite Element Analyses - Giovanni Conni, Department of Mechanical Engineering [Leuven], Department of Computer Science [Leuven]  
15:26 - 15:28 › Dictionary-based Online-adaptive Structure-preserving Model Order Reduction for Parametric Hamiltonian Systems - Robin Herkert, University of Stuttgart  
15:28 - 15:30 › Dynamical low rank approximation and parametric reduced order models for shallow water moment equations - Julian Koellermeier, University of Groningen - Philipp Krah, Technical University of Berlin - Jonas Kusch, University of Innsbruck  
15:30 - 15:32 › Effective A-posteriori Error Estimation for Port-Hamiltonian Systems - Johannes Rettberg, University of Stuttgart, Institute of Engineering and Computational Mechanics  
15:32 - 15:34 › Efficient Hyper-Reduction of contact problems treated by Lagrange multipliers. - Simon LE BERRE, Laboratoire de simulation du combustible, CEA/DES/IRESNE/DEC/SESC  
15:34 - 15:36 › Hermite kernel surrogates for the value function of high-dimensional nonlinear optimal control problems - Tobias Ehring, University of Stuttgart  
15:36 - 15:38 › Hybrid Projection Methods with Recycling for Inverse Problems - Jiahua Jiang, University of Birmingham  
15:38 - 15:40 › Interpolatory (P)MOR via low-rank (tensor) approximation in general linear matrix equations - Jens Saak, Max Planck Institute for Dynamics of Complex Technical Systems  
15:40 - 15:42 › Low-rank methods in large scale constrained optimization - Martin Stoll, TU Chemnitz  
15:42 - 15:44 › Low-rank Parareal: a low-rank parallel-in-time integrator - Benjamin Carrel, Department of Mathematics, University of Geneva  
15:44 - 15:46 › M-M.E.S.S. 3.0 - Introducing Krylov-based solvers - Jens Saak, Max Planck Institute for Dynamics of Complex Technical Systems  
15:46 - 15:48 › Machine learning-based reduced order modelling: Towards intelligent digital twins - George Drakoulas, FEAC Engineering P.C., Department of Mechanical Engineering & Aeronautics, University of Patras  
15:48 - 15:50 › Mass-conserving and energy-consistent ROMs for the incompressible Navier-Stokes equations with time-dependent boundary conditions - Henrik Rosenberger, Centrum Wiskunde & Informatica  
15:50 - 15:52 › Model order reduction for wave-type problems with band-limited outputs of interest - Muhammad Hamza Khalid, Department of Applied Mathematics, University of Twente  
15:52 - 15:54 › Model order reduction via substructuring for a nonlinear, switched, differential-algebraic machine tool model - Julia Vettermann, Technische Universität Chemnitz, Research Group Mathematics in Industry and Technology  
15:54 - 15:56 › Multi-fidelity Optimization of an Acoustic Metamaterial using Model Order Reduction and Machine Learning - Sebastian Schopper, Gerhard Müller  
15:56 - 15:58 › Non-intrusive adaptive surrogate modeling of parametric frequency-response problems - Davide Pradovera, Faculty of Mathematics, University of Vienna, CSQI, EPFL  
15:58 - 16:00 › On Balanced Truncation Error Bound and Sign Parameters - Sean Reiter, Department of Mathematics, Virginia Tech  
16:00 - 16:02 › On the use of exponential integrators for large-scale Hamiltonian systems - Michel-Niklas Senn, TU Braunschweig  
16:02 - 16:04 › Operator inference method for mechanical systems - Yevgeniya Filanova, Max-Planck-Institut für Dynamik Komplexer Technischer Systeme  
16:04 - 16:06 › Parametric model order reduction approach for quasi-static non-linear mechanical problems using an industrial code: application to an elasto-plastic material - Eki Agouzal, Inria Bordeaux - Sud-Ouest, Team Memphis, Institut de Mathématiques de Bordeaux, EDF R&D  
16:06 - 16:08 › pyMOR - Model Order Reduction with Python - Stephan Rave, University of Münster  
16:08 - 16:10 › Reduced Basis Methods for Time-Harmonic Maxwell's Equations - Anna Sanfilippo, University of Trento  
16:10 - 16:12 › Reduced order models for efficient uncertainty quantification of wooden structures with inhomogeneous material properties - Catharina Czech, Technical University of Munich  
16:12 - 16:14 › Reduction of single phase flow models in porous media using a quantity of interest - Jana Tarhini, IFP Energies Nouvelles  
16:14 - 16:16 › ROM for Large-scale Modelling of Urban Air Pollution - Moaad Khamlich, SISSA Scuola Internazionale Superiore di Studi Avanzati  
16:16 - 16:18 › Spectral approximation of Lyapunov operator equations with applications in high dimensional non-linear feedback control - Bernhard Höveler, Technische Universität Berlin  
16:18 - 16:20 › Subspace-Distance-Enabled Active Learning for Parametric Model Order Reduction of Dynamical Systems - Harshit Kapadia, Max Planck Institute for Dynamics of Complex Technical Systems  
16:20 - 16:22 › Symplectic formulation of PGD reduced-order models for structural dynamics applications - Clement Vella, LaMcube  
16:22 - 16:24 › Tensor Galerkin Proper Orthogonal Decomposition for Uncertainty Quantification of PDEs with Random Parameters - Jan Heiland, Max Planck Institute for Dynamics of Complex Technical Systems  
16:24 - 16:26 › Time extrapolation technique applied to POD-based ROM - Pablo Solán-Fustero, University of Zaragoza  
16:26 - 16:28 › Towards a Benchmark Framework for Model Order Reduction in the Mathematical Research Data Initiative (MaRDI) - Kathryn Lund, Max Planck Institute for Dynamics of Complex Technical Systems  
16:30 - 17:00 Coffee break (H 3005/3006)  
17:00 - 18:00 Poster Session (H 3006)  
19:30 - 23:30 Conference Dinner (Wartehalle)  

Thursday, September 22, 2022

Time Event (+)
08:30 - 09:15 Plenary (H 1012) - Chair: Ralf Zimmermann (+)  
08:30 - 09:15 › Digital Twins through Reduced Order Models and Machine Learning - Jan S. Hesthaven, Ecole Polytechnique Fédérale de Lausanne  
09:20 - 10:35 Session 5 (H 1012) - Chair: Ralf Zimmermann (+)  
09:20 - 09:45 › Optimization based model order reduction for stochastic systems - Melina Freitag, Institute of Mathematics  
09:45 - 10:10 › Balanced Truncation for Bayesian Inference - Elizabeth Qian, California Institute of Technology  
10:10 - 10:35 › Semi-supervised Invertible DeepONets for Bayesian Inverse Problems - Sebastian Kaltenbach, Technical University of Munich  
10:35 - 11:00 Coffee break (H 3005/3006)  
11:00 - 12:40 Parallel Session 6a (H 1012) - Chair: Tobias Breiten (+)  
11:00 - 11:25 › Structured barycentric forms and their application to iterative data-driven model reduction of second-order systems - Ion Victor Gosea, Max-Planck-Institute Magdeburg  
11:25 - 11:50 › Structured L2-Optimal Parametric Model Order Reduction - Petar Mlinarić, Virginia Tech  
11:50 - 12:15 › High Order Approximations of the Operator Lyapunov Equation Have Low Rank - Luka Grubisic, University of Zagreb, Faculty of Science  
12:15 - 12:40 › Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models - Stefania Fresca, Politecnico di Milano  
11:00 - 12:40 Parallel Session 6b (H 1058) - Chair: Mario Ohlberger (+)  
11:00 - 11:25 › Hybrid fluid/particle methods for kinetic equations describing neutral particles in nuclear fusion plasma-edge modelling - Vince Maes, KU Leuven, Department of Computer Science  
11:25 - 11:50 › Model Order Reduction in Contour Integral Methods for parametric PDEs - Mattia Manucci, Gran Sasso Science Institute  
11:50 - 12:15 › Inf-Sup-Constant-Free Error Estimation for Linear Parametric Systems - Sridhar Chellappa, Max Planck Institute for Dynamics of Complex Technical Systems  
12:15 - 12:40 › Parametric reduced order modelling for transport dominated systems via shifted POD deep learning models - Shubhaditya Burela, Technical University of Berlin  
12:40 - 14:00 Lunch  
14:00 - 14:45 Plenary (H 1012) - Chair: Serkan Gugercin (+)  
14:00 - 14:45 › Port-Hamiltonian systems -- from a general modelling wishlist to surrogate models with guarantees - Benjamin Unger, SC Sim Tech, University of Stuttgart  
14:50 - 16:05 Session 6 (H 1012) - Chair: Serkan Gugercin (+)  
14:50 - 15:15 › Registration-based model reduction of parameterized PDEs with spatio-parameter adaptivity - tommaso taddei, Inria Bordeaux South-West  
15:15 - 15:40 › A certified wavelet-based physics-informed neural network for nonlinear model reduction of parameterized partial differential equations - Lewin Ernst, Institute for Numerical Mathematics, Ulm University  
15:40 - 16:05 › Neural networks investigation of bifurcating phenomena in fluid-dynamics - Federico Pichi, EPFL, École Polytechnique Fédérale de Lausanne, MCSS, Route Cantonale, 1015, Lausanne, Switzerland.  
16:05 - 16:30 Coffee break (H 3005/3006)  
16:30 - 17:45 Session 7 (H 1012) - Chair: Benjamin Peherstorfer (+)  
16:30 - 16:55 › Deep Orthogonal Decomposition via Mesh-Informed Neural Networks for Reduced Order Modeling of parametrized PDEs - Nicola Franco, Modeling and Scientific Computing [Milano]  
16:55 - 17:20 › An adaptive hierarchy of certified machine learning and reduced basis surrogates for parametrized PDEs - Felix Schindler, Mathematics Münster, University of Münster  
17:20 - 17:45 › Multiscale modeling of heterogeneous structures based on a localized model order reduction approach - Philipp Diercks, Bundesanstalt für Materialforschung und -prüfung (BAM)  

Friday, September 23, 2022

Time Event (+)
08:30 - 09:15 Plenary (H 1012) - Chair: Karsten Urban (+)  
08:30 - 09:15 › Deep learning for reduced order modeling - Andrea Manzoni, MOX - Department of Mathematics, Politecnico di Milano  
09:20 - 10:35 Session 8 (H 1012) - Chair: Karsten Urban (+)  
09:20 - 09:45 › Generating reduced order models parallel in time via random sampling - Julia Schleuß, Faculty of Mathematics and Computer Science, University of Münster  
09:45 - 10:10 › The local sample complexity of non-linear least squares approximation - Philipp Trunschke, Nantes Université  
10:10 - 10:35 › Randomized local model order reduction for nonlinear PDEs - Kathrin Smetana, Stevens Institute of Technology  
10:35 - 11:00 Coffee break (H 3005/3006)  
11:00 - 11:50 Session 9 (H 1012) - Chair: Sara Grundel (+)  
11:00 - 11:25 › Context-aware learning of low-dimensional stabilizing controllers in the scarce data regime - Steffen W. R. Werner, Courant Institute of Mathematical Sciences, New York University  
11:25 - 11:50 › Slow collective dynamics via data-driven approximation of the Koopman generator - Feliks Nüske, Max Planck Institute for Dynamics of Complex Technical Systems  
11:55 - 12:40 Plenary (H 1012) - Chair: Sara Grundel (+)  
11:55 - 12:40 › Structure-preserving reduced-order models for parametric cross-diffusion systems - Virginie Ehrlacher, CERMICS, Paris  
12:40 - 12:50 Closing (H 1012)