Confirmed Minisymposia

Proposals for Minisymposia (including your name, affiliation, MS title and a short minisymposium description) should be sent via e-mail to the Conference Secretariat at
Minisymposium 1
"Computational Multiscale Modelling under Uncertainty"
Paul Steinmann (Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany)
Dmytro Pivovarov (Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany)
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This MS is intended to foster development and interaction in the area of computational multiscale modelling with uncertainties (aleatoric and/or epistemic). Thus novel computational contributions to different non-deterministic simulation techniques with wide application to multiscale and multiphysics problems are invited. These problem classes may e.g. cover stochastic and heterogeneous elasticity, plasticity, generic rheology, heat conduction, magneto- and electro-statics, etc., and their various couplings. Thereby possible multiscale simulation techniques may typically build on (but are certainly not restricted to) e.g.:

  • Stochastic Galerkin Method
  • Monte-Carlo Simulation
  • Stochastic Collocation Method
  • Perturbation Methods
  • Imprecise Probability Modeling
  • Fuzzy, Fuzzy-Stochastic, and Interval-Stochastic Modeling
  • Constitutive Modelling under Uncertainty
  • Direct Numerical Simulations of Non-ergodic Media
  • Others
Minisymposium 2
"Bayesian analysis of numerical models"
Iason Papaioannou (Engineering Risk Analysis Group, Technische Universität München, Germany)
Daniel Straub (Engineering Risk Analysis Group, Technische Universität München, Germany)
Costas Papadimitriou (Department of Mechanical Engineering, University of Thessaly, Greece)
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In computational science and engineering, numerical models of physical systems are constructed with aim at reproducing experimental observations. The parameters of the numerical models are determined by combining information from different sources such as direct measurements of the parameters or the system behavior, expert knowledge, categorical data and information from literature. In probability theory, the process of combining information to learn model parameters is formalized in the concept of Bayesian updating. Thereby, the prior probability distribution of the model parameters is updated with new data to a posterior distribution. The derived distribution can be further used for forward uncertainty propagation and reliability assessment of the system performance.

This mini-symposium aims to attract papers that address either methodological developments or novel applications on Bayesian analysis of numerical models. Individual relevant topics include: Markov chain Monte Carlo methods; sequential Montel Carlo methods; Taylor series approximations to the posterior; Kriging/Gaussian process models; conjugate priors and Gibbs sampling; approximate Bayesian computation; Bayesian updating with surrogate models; multilevel/multifidelity methods; structural identification; reliability updating; updating in the presence of spatial/time variability; applications that investigate the influence of prior considerations on the analysis results; definition of the likelihood function; representation of model errors; optimal experimental design.

Minisymposium 3
"Uncertainty Quantification in Vibration based Monitoring and Structural Dynamics Simulations"
Vasilis Dertimanis (Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Switzerland)
Eleni Chatzi (Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Switzerland)
Costas Papadimitrou (Department of Mechanical Engineering, University of Thessaly, Greece)
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Due to factors related to manufacturing or construction processes, ageing, loading, environmental & boundary conditions, measurement errors, modeling assumptions / inefficiencies and numerous others, almost every engineering system is characterized by uncertainty. The propagation of uncertainty through the system gives rise to corresponding complexities during simulation of its structural response, yet also during its characterization based on experimental data. Consequently, only a limited degree of confidence can be attributed in the behavior, reliability and safety of structural systems in particular throughout their life cycle. For this purpose, it is imperative to develop models and processes able to encompass the aforementioned uncertainties. 

This mini-symposium deals with uncertainty quantification and propagation methods applicable to the simulation and identification of complex engineering systems. It covers theoretical and computational issues, applications in structural dynamics, earthquake engineering, mechanical and aerospace engineering, as well as other related engineering disciplines. Topics relevant to the session include: dynamics of structural systems, structural health monitoring methods for damage and reliability prognosis, theoretical and experimental system identification for systems with uncertainty, uncertainty quantification in model selection and parameter estimation, stochastic simulation techniques for state estimation and model class selection, structural prognosis techniques, updating response and reliability predictions using data. Papers dealing with experimental investigation and verification of theories are especially welcomed.

Minisymposium 4
"Inverse methods for Uncertainty Quantification in large-scale applications"
Matthias Faes (KU Leuven , Belgium)
David Moens (KU Leuven, Belgium)
Michael Hanss (University of Stuttgart, Germany)
Michael Beer (Leibniz University, Germany)
Matteo Broggi (Leibniz University, Germany)
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Recent developments in non-deterministic modelling approaches introduced a very broad spectrum of highly advanced numerical methods for reliability analysis and uncertainty quanti cation, including probabilistic, interval, fuzzy or imprecise methods. The application of these techniques however requires the analyst to accurately specify the relevant uncertain model parameters. Since direct measurement of such model quantities is often not feasible, or in practice too expensive, inverse techniques are commonly applied.

This mini-symposium aims to gather experts researchers, academics and practising engineers concerned with inverse methods for uncertainty quantification to present their recent ndings, methodological developments and innovative applications. Papers discussing advances in techniques from both frequentist and Bayesian interpretations of probability theory, as well as interval and possibilistic methods and concepts based on imprecise probabilities are invited.

Minisymposium 5
"Surrogate and reduced-order modeling for stochastic simulation of physical systems"
Michael D. Shields (Johns Hopkins University, United States)
Bruno Sudret (ETH Zürich, Switzerland)
Alexandros Taflanidis (University of Notre Dame, United States)
Dimitrios Giovanis (Johns Hopkins University, United States)
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The use of high-dimensional physics-based computational models (high-fidelity models) is critical to assessing the behavior of complex physical systems. Given uncertainties in the model input and/or parameters, the problem is set in a probabilistic framework in order to quantify the uncertainty in the behavior/response of the physical system. However, this comes at great computational cost as it requires many repeated simulations at sample points spanning the, often high dimensional, uncertain parameter space.  Computational constraints motivate the need to carefully select the sample points at which the solution is evaluated and then use these points to build a surrogate and/or reduced order model. A surrogate model is a simpler mathematical model describing the physical system that is inexpensive to evaluate and accurately approximates/ interpolates the solution between the sample points where the exact solution is known. A reduced-order model (ROM), on the other hand, aims to preserve the governing equations while drastically reducing the number of degrees of freedom. This mini-symposium aims to attract papers that address either methodological developments or novel applications on reduced-order and surrogate models used in the framework of uncertainty quantification. Individual relevant topics include:

  • Stochastic collocation methods
  • Generalized polynomial chaos expansions
  • Gaussian process regression / kriging
  • Machine learning and artificial neural networks
  • Reduced basis and nonlinear projection-based ROMs
  • Adaptive sampling methods for efficient surrogate generation 
  • High dimensional sampling and interpolation
  • Novel applications in mechanics, materials, and other relevant fields.
Minisymposium 6
"Uncertainty Computations with Reduced Order Models and Low-rank Representations"
Hermann G. Matthies (Technische Universität Braunschweig, Germany)
Bojana Rosic (Technische Universität Braunschweig, Germany)
Elmar Zander (Technische Universität Braunschweig, Germany)
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Keywords: Uncertainty quantification, reduced order models, low-rank

Computational Models with uncertain resp. probabilistic elements as they occur in uncertainty quantification and Bayesian updating lead to high-dimensional problems and require a high computational effort. This motivates the desire to employ reduced order models (ROMs) and low-dimensional representations of high-dimensional functions in order to reduce the computational demands. This minisymposium will be devoted to the interplay of the computational demands from the envisaged uncertainty computation and the requirements which stem from the underlying physical model. This includes approaches which merely try to approximate some output of the computational model – a quantity of interest – using structured approximations (such as sparsity or low-rank) and many different paradigms for their construction (interpolation, statistical learning, etc). Also included are approaches which first try to approximate the state of the system in a reduced form, where the governing physical equations are satisfied in some weak sense for all realisations, and from this representation of the state derive approximations of the quantity of interest. Possible applications are uncertainty quantification, estimation of rare events, Bayesian updating, stochastic control and optimisation.

Minisymposium 7
"Surrogate models: benchmark problems and solutions"
Jean-Marc Bourinet (SIGMA Clermont, France)
Sankaran Mahadevan (Vanderbilt University, United States)
Nicola Pedroni (Politecnico di Torino, Italy)
Bruno Sudret (ETH Zurich, Switzerland)
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The use of surrogate models has become quite popular in uncertainty propagation (UP) of costly-to-evaluate physical models. Several types of surrogate models are available, with their own advantages and limits. These models are applied to a large variety of UP problems, such as reliability analysis, sensitivity analysis and optimal design under uncertainty among others. Efforts are made to take the best of such techniques, in order to improve the efficiency of the devised algorithms: adaptive enrichment strategies, dimension reduction, multi-fidelity models, ensemble of surrogates, surrogate-based sampling, … The objective of this Mini-Symposium is to discuss the requirements of surrogate-based approaches (in terms of code implementation, computer resources needed, parameter tuning), their applicability to challenging problems (e.g. high dimensional input spaces, complex physical models, systems) and their efficiency/robustness with respect to other reference techniques. The proposal of benchmark physical models challenging for surrogate-based approaches is expected from the contributed papers, with a clear model description and reference solutions.

Minisymposium 8
"Current Topics in Uncertainty Characterization, Optimization and Design"
Ivi C. Tsantili (Technical University of Crete, Greece)
Dionissios T. Hristopulos (Technical University of Crete, Greece) 
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In this mini symposium we will discuss recent advances in uncertainty characterization, order reduction as well as optimization and design under uncertainty. For these topics we will focus on new models (e.g. physically motivated covariance Kernels) or frameworks that overcome simplifying and often unnecessary assumptions such as the Gaussian assumption, the temporal and spatial separability, as well as models defined over rich parameter spaces that can efficiently capture space-time data dependencies and cross-correlations. We will explore the connection of the above with machine learning approaches, multivariate formulations (e.g., co-kriging, multivariate covariance functions), sensitivity analysis, as well as optimization and design under uncertainty (e.g., wave propagation in random media).

Minisymposium 9
"Uncertainty Propagation and Quantification with computationally expensive models"
Laurent van den Bos (CWI Amsterdam, Netherlands)
Yous van Halder (CWI Amsterdam, Netherlands)
Benjamin Sanderse (CWI Amsterdam, Netherlands)
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In many engineering problems from the field of uncertainty quantification the interest is in the effect of uncertainty on a specific Quantity of Interest (QoI), that is only known implicitly as the solution of some equations (e.g. a system of partial differential equations). As a result, it holds that determining a numerical value of the QoI for a specific realization of the uncertain parameters is computationally expensive. This limits the applicability of straightforward uncertainty propagation and quantification methods, as the number of evaluations of the underlying model must remain small to keep the approach tractable.

An example of such a problem is predicting the effect of uncertain boundary conditions on the characteristics of fluid flow, also known as a forward problem. Another, but not less important example, is calibrating the boundary conditions from noisy (i.e. "uncertain") measurements, which is referred to as an inverse problem.

In this minisymposium, recent developments and practical techniques are discussed to numerically approximate solutions for both the forward and the inverse problem. The focus is on non-intrusive approaches, i.e. on efficient collocation techniques, bringing together researchers working on: efficient sampling, surrogate models, machine learning techniques, etc. A common theme in all approaches is the limited number of possible model evaluations.

Minisymposium 10
"Software for Uncertainty Quantification"
Stefano Marelli (ETH Zürich, Switzerland)
Edoardo Patelli (University of Liverpool, United Kingdom)
Dirk Pflüger (University of Stuttgart, Germany)
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Uncertainty quantification (UQ) has become a central topic in virtually all fields of applied sciences over the past decade. Including uncertainty quantification in predictive models is a technical challenge that fostered the development of techniques such as (non-)probabilistic modelling of the sources of uncertainty, surrogate modelling, sensitivity analysis, model calibration, robust optimization, etc. The deployment and further diffusion of such techniques to a larger audience, however, depends critically on the availability of proper software that be incorporated by researchers and practioners into their own workflows.

This minisymposium aims at bringing together leading and innovative players in the international UQ software scene to foster discussions and exchange of ideas between developers and prospective users. Contributions are welcome on the following topics: non-intrusive UQ techniques, surrogate modelling, HPC in UQ, general-purpose UQ software, dimensionality reduction techniques and case studies and applications to real-csale industrial problems.

Minisymposium 11
"Polymorphic uncertain data for numerical analysis and design of structures"
Michael Kaliske (Technische Universität Dresden, Germany)
Wolfgang Graf (Technische Universität Dresden, Germany)
Sigrid Leyendecker (Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany)
Stefanie Reese (RWTH Aachen, Germany)
Wolfgang Wall (Technical University of Munich, Germany)
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Key words: Data Uncertainty and Modelling, Computational Mechanics, Numerical Design

The numerical analysis and design of structures is currently characterised by deterministic thinking and methods. Deterministic modelling of the reality indicates preciseness and safety, while, on contrary all available data and information are characterized by uncertainty (variability, imprecision, incompleteness), which cannot be neglected.

The main focus is the presentation of methods for the numerical simulation of structures under consideration of data uncertainty. With the help of the mini-symposium, the opportunities of inter- and transdisciplinary shall be used for the generation of synergies between mathematics and engineering sciences.

Engineering solutions are characterized by inherent robustness and flexibility as essential features for a faultless life of structures and systems under uncertain and changing conditions. An implementation of these features in a structure or system requires a comprehensive consideration of uncertainty in the model parameters and environmental and man imposed loads as well as other types of intrinsic and epistemic uncertainties. Numerical design of structures should be robust with respect to (spatial and time dependent) uncertainties inherently present in resistance of materials, boundary conditions etc.

This requires in turn the availability of a reliable numerical analysis, assessment and prediction of the lifecycle of a structure taking explicitly into account the effect of the unavoidable uncertainties.
Challenges in this context involve, for example, limited information, human factors, subjectivity and experience, linguistic assessments, imprecise measurements, dubious information, unclear physics etc. Due to the polymorphic nature and characteristic of the available information both probabilistic and set-theoretical approaches are relevant for solutions.

This mini-symposium aims at bringing together researchers, academics and practicing engineers concerned with the various forms of advanced engineering designs. Recent deve-lopments of numerical methods in the field of engineering design which include a comprehensive consideration of uncertainty and associated efficient analysis techniques, such as advanced Monte Carlo simulation, meta-model approximations, and High Performance Computing strategies are explicitly invited. These may involve imprecise probabilities, interval methods, Fuzzy methods, and further concepts. The contributions may address specific technical or mathematical details, conceptual developments and solution strategies, individual solutions, and may also provide overviews and comparative studies.

Particular attention should be paid to practical applicability in engineering. Besides the applications of the involved engineering sciences, “real world” scenarios should be presented. The distinction between early stage of design and final design is significant.

Minisymposium 12
"Uncertainty quantification using experimental data"
Kheirollah Sepahvand (Technical University of Munich, Germany)
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The theoretical and numerical aspects of uncertainty quantification (UQ) in engineering and science have been well developed in past decades, however, they suffer the lack of accurate experimental data for validation.

This minisymposium focusses on the practical UQ when experimental data on uncertain parameters and structural responses are available. The challenges in this context involve, for instance, robust stochastic inverse methods for UQ identification when limited data are available; probability estimation from experimental data; spectral representation of spatially random fields using measured data; etc.

The goals aim to bring together researchers, academics and industry experts in various areas of engineering and science to discuss the latest and recent methods and techniques combining experimental UQ with theoretical and numerical methods. Contributions addressing UQ in experimental structural dynamics, vibration analysis, modal analysis, acoustic problems, earthquake engineering and risk analysis are particularly welcome.