MOR Seminar

SimTech Seminar on Model Reduction and Data Techniques for Surrogate Modelling




Thursday, 14:00, PWR 7, V 7.12


ITM: Jun.-Prof. Dr. Jörg Fehr
IAM (CE): Dr. Felix Fritzen
IANS: Prof. Dr. Bernard Haasdonk


SimTech-PhD students, General interested audience in MOR, Surrogate Modelling, Control and Real-time Simulation from academia as well as industry.


Since 2009, this seminar represents a general platform for talks and exchange in the field of surrogate modelling, in particular Model Order Reduction (MOR) as well as novel data-based techniques in simulation science. Both methodological as well as application oriented presentations highlight the various aspects and the relevance of surrogate modelling in mathematics, technical mechanics, material science, control theory and other fields. We aim both at university members, as well as external persons from science and industry. The seminar is organized by three institutes and represents an activity of the SimTech.

The presentations are announced some days in advance via the mor-seminar mailing list of the University of Stuttgart.In case of interest to join this mailing list, please contact the organizers.

Presentations WS 2019/2020

Program Flyer

Tensor approximation meets model order reduction

Certified Reduced-Order Modeling for Multiobjective, Nonsmooth and Stochastic Optimizatio

Reduced Order Modeling via Computer Vision in Solid Mechanics

Presentations SS 2019

Program Flyer

"Thermal model order reduction considering heat radiation"

"A priori fluctuation modes for microstructures assembled by means of Wang tiles"

" A semi-incremental scheme for fatigue damage computations"

Presentations WS 2018/2019

Programm Flyer

"Modeling and Control of Tendon-driven Elastic Continuum Mechanisms"

Abstract: In modern robots, joint-mechanisms that are built to interact with the environment 
usually features intrinsic passive compliance. Based on this design paradigm, elastic continuum 
mechanism are also applied frequently. Actively controlling the pose of the mechanism is
indispensable in robots. However, the soft structure reacts to any kind of external loading or 
disturbance. An accurate model that captures all intended deformations is usually computational 
expensive and not applicable in real time control. Therefore, this talk will deal with reduced 
models for such kind of system that allows for their capability analysis and for model-based control.

"Artificial Neural Network Surrogate Models in Structural Mechanics"

Artificial neural network surrogate models can be applied to several fields in structural engineering, e.g. to replace time consuming finite element simulations for structural optimization, reliability assessment, sensitivity analysis, system and parameter identification, structural health monitoring, real-time simulations for computer aided steering of structural processes, and structural control. This lecture contains an overview on applications of artificial neural networks in structural mechanics. Feedforward and recurrent network architectures and corresponding training algorithms are discussed. Examples for neural network based surrogate modelling of computationally expensive structural models are presented. Also the possibility of neural network based material models within the finite element method is shown. In addition, strategies are discussed to consider uncertainties of structural and material parameters within artificial neural network approaches.

Data Driven Parametric Modeling in Discrete Least Squares Norm

Presentations SS 2018

"Space-Time Model Order Reduction for nonlinear path-dependent long-term and cyclic processes"

"Efficient Large Strain Homogenization: Reduced Bases and High-dimensional Interpolation"

"Nonlinear model order reduction for explicit dynamics"

"The Reduced Basis Method for Parameter Functions and Application in Quantum Mechanics"

Presentations WS 2017/2018

"Using Feedthrough to avoid unphysical frequencies in reduced systems"

Abstract: Almost all linear model order reduction schemes for mechanical systems achieve static correctness or local precision by adding static mode shapes to the reduction basis. Since this basis is used to project mass and stiffness matrix, these static mode shape develop a entirely unphysical frequency in the reduced system which may cause serious problems if these frequencies are excited. Instead of achieving static correction by using static correction modes, a simple addition to the spectral sum is proposed. This approach has several advantages: The number of degrees of freedom is further reduced, unphysical dynamics are  eliminated, the reduction is still statically correct and the numerical  efficiency increases considerably. The potential and advantages of the approach will be discussed and demonstrated for numerical test examples.

"Greedy algorithms for optimal measurements selection in state estimation using reduced models"

Abstract: In this talk, we will talk about recent techniques developed to estimate the state of a physical system using sensor measurements and reduced models. After giving a short overview on the methodology and the approximation results, we will explain how we can use the methodology in order to select the sensors to place in the physical system in an optimal way. If time permits, we will also discuss the challenges posed when the the sensor measurements are no longer exact but polluted by noise. This is a work in collaboration with P. Binev, A. Cohen and J. Nichols.

"Automatic derivation of material laws for simulating structural components"

Abstract: In our talk we will present a novel approach to automatically derive material laws by model order reduction methods (MOR) for the component simulation of fiber reinforced plastic (FRP) materials, which is based on the output of
an injection or compression moulding simulation.

"Structure Preserving Model Reduction for Linear Elasticity"

Presentations SS 2017

"Structure-Preserving Model-Reduction"

Abstract: Reduced basis methods are popular for approximately solving large and complex systems of differential equations. However, many challenges remain to secure the flexibility, robustness, and efficiency needed for general large-scale applications, in particular for nonlinear and/or time-dependent problems. In this talk, we present a greedy approach for the construction of a reduced system that preserves the geometric structure of Hamiltonian systems. Preserving the Hamiltonian structure ensures the stability of the reduced system over long-time integration. The performance of the approach is demonstrated for both ODEs and PDEs. We then discuss how the method can be extended to preserve the symmetries and intrinsic structures of dissipative problems through the notion of port-Hamiltonian systems.

 "Controlling of the model reduction error in FE2 analysis of transient heat flow"

 "Variational Inertia Scaling for Explicit Dynamics"

"Kernel Methods for Nonlinear Control and Random Dynamical Systems"

Presentations WS 2016/2017

"Error Controlled Nonlinear Model Reduction Techniques for Crash Simulations"

"A Newton-Euler approach to modelling and control of flexible manipulators"

"Homogenization of viscoplastic composites based on the complementary TFA"

14:15, PWR 5a, 0.015
Prof. Sonia Marfia (University of Cassino and Southern Lazio)

"A nonuniform TFA homogenization technique based on piecewise interpolation functions of the inelastic field"

"Kernel Methods for Accelerating Implicit Integrators"

Presentations SS 2016

"Reduced Basis Approximation of the time-discrete Algebraic Riccati Equation"

"Robust optimization of permanent magnet synchronous machines using model order reduction for the efficient computation of local and global sensitivities"

"Efficient finite element simulation for cyclic loads with a viscoelastic-viscoplastic-damage material model"

"Nonlinear modes and their suitability for model order reduction"

"Application of model order reduction techniques to the lubricated contact of elastic bodies"


Bernard Haasdonk
Prof. Dr.

Bernard Haasdonk

Head of Group
Dean of Studies (B.Sc./M.Sc. Mathematik)

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