Mon 16 Nov
Uncertainty Treatment and Optimisation in Aerospace Engineering (UTOPIAE)
Prof Massimiliano Vasile, Aerospace Centre of Excellence, University of Strathclyde
UTOPIAE is a European research and training network, funded through EC H2020, looking at cutting edge methods bridging optimisation and uncertainty quantification applied to aerospace systems.
UTOPIAE drives to develop fundamental mathematical methods and algorithms to bridge the gap between Uncertainty Quantification and Optimisation and between Probability Theory and Imprecise Probability Theory for Uncertainty Quantification to efficiently solve high-dimensional, expensive and complex engineering problems.
UQOP is the flagship international conference organised by UTOPIAE, and will include talks and papers on the latest work in the fields.
Trajectory design and optimisation under epistemic uncertainty
10:15-10:45 Simão da Graça Marto
Multi-Objective Robust Trajectory Optimization of Multi Asteroid Fly-By Under Epistemic Uncertainty
10:45-11:15 Cristian Greco
Belief Optimal Control for Robust Impulsive Trajectory Design
Optimisation under uncertainty using reliability, robustness or resilience metrics
11:30-12:00 Alberto Clarich, Rosario Russo
Reliability-based Robust Design Optimization of a Jet Engine Nacelle
12:00-12:30 Hoai Phuong Le, Juergen Branke
Bayesian Optimization for Robust Solutions under Uncertain Input
12:30-13:00 Gianluca Filippi
Network Resilience Optimisation of Complex Systems
Surrogate-based uncertainty quantification in aerospace optimisation
14:15-14:45 Elisa Morales Tirado
Gaussian Processes for CVaR approximation in Robust Aerodynamic Shape Design
14:45-15:15 Christian Sabater
Optimization under Uncertainty of Shock Control Bumps for Transonic Wings
Probabilistic Machine Learning for Simulation-Based Engineering Design and Data-Driven Failure Prognostics
Prof Chao Hu, Iowa State University
Since the 1980s, major industries and government agencies worldwide have faced increasing challenges in ensuring the reliability and safety of engineered systems. In response to these challenges, considerable research attention has been directed towards developing probabilistic design and monitoring methods for preventing failures of engineered systems. This talk will introduce probabilistic machine learning techniques for simulation-based engineering design and data-driven failure prognostics. An emphasis of this talk will be placed on tackling the uncertainty issues in reliability analysis in the system design stage and remaining useful life prediction in the system operation stage. The probabilistic machine learning techniques will be demonstrated using two industry applications, namely (i) design under uncertainty of energy harvesting device and (ii) capacity forecasting and prognostics of lithium-ion battery.
16:45-17:15 Peter Zeno Korondi
Multi-objective design optimisation of an airfoil with geometrical uncertainties leveraging multi-fidelity Gaussian process regression
17:15-17:45 Lorenzo Gentile
High-Lift Devices Topology Robust Optimisation using Surrogate Based Optimisation
Wrap-up by symposium chairs:
Dr Stefan Goertz (DLR, Germany)
Dr Mariapia Marchi (ESTECO, Italy)
Prof Boris Naujoks (THK, Germany)
Tue 17 Nov
On nonlinear semigroups and model uncertainty in finance
Prof Max Nendel
Universität Bielefeld, Center for Mathematical Economics (IMW)
In mathematical finance, model uncertainty or ambiguity is an almost omnipresent phenomenon, which, for example, appears, when certain aspects of an underlying asset cannot be determined precisely, or in the absence of sufficient data in order to perform reliable statistical estimation methods for the parameters of a stochastic process. The latter typically leads to so-called parameter uncertainty in the generator of the stochastic process. Under this type of uncertainty, worst case considerations together with dynamic consistency requirements lead to a stochastic optimal control problem, where, roughly speaking, “nature” tries to control the system into the worst possible scenario. In this talk, we illustrate how a class of optimal control problems arising in this context can be tackled using a semigroup-theoretic approach and give rise to imprecise versions of stochastic processes. We conclude by considering a second approach, where the uncertainty is captured in terms of a Wasserstein distance around a reference model.
Computing bounds for imprecise continuous-time Markov chains using normal cones
Prof Damjan Skulj, University of Ljubljana
The theory of imprecise Markov chains has achieved significant progress in recent years. Its applicability, however, is still very much limited, due in large part to the lack of efficient computational methods for calculating higher-dimensional models. The high computational complexity shows itself especially in the calculation of the imprecise version of the Kolmogorov backward equation. The equation is represented at every point of an interval in the form of a minimization problem, solvable merely with linear programming techniques. Consequently, finding an exact solution on an entire interval is infeasible, whence approximation approaches have been developed. To achieve sufficient accuracy, in general, the linear programming optimization methods need to be used in a large number of time points.
The principal goal of this talk is to provide a new, more efficient approach for solving the imprecise Kolmogorov backward equation. It is based on the Lipschitz continuity of the solutions of the equation with respect to time, causing the linear programming problems appearing in proximate points of the time interval having similar optimal solutions. This property is exploited by utilizing the theory of normal cones of convex sets. The present article is primarily devoted to providing the theoretical basis for the novel technique, yet, the initial testing shows that in most cases it decisively outperforms the existing methods.
11:30-12:00 Alexander Erreygers
Extending the domain of imprecise continuous-time Markov chains
12:00-12:30: Natan T’Joens
Characterising Game-Theoretic Upper Expectations in Discrete-Time Finite-State Stochastic Processes
12:30-13:00: Thomas Krak
Computing Expected Hitting Times for Imprecise Markov Chains
Imprecise probability for reliable and robust engineering analysis
Prof Edoardo Patelli, University of Strathclyde
The assessment of modern engineering systems and in particular for critical infrastructures is often performed with incomplete information, missing data and large unknowns. Strong initial assumptions may be needed to use classical probabilistic methods especially for cases affected by a lack of data. However, these assumptions can deeply influence the final results and lead to severe risk misjudgement by distorting the information regarding the true level of uncertainty affecting the system safety and giving a false sense of confidence. Furthermore, this classical implementation does not differentiate between different type of uncertainty. This is a severe limitation as it makes the analyst unable to grasp how much of the uncertainty is due to inherent variability and to what extent the uncertainty is due to poor data quality (therefore suitable to be reduced in principle).
Imprecise probability approaches have been introduced to better deal with scarce or limited information by adopting a more robust characterisation and propagation of different representation of the uncertainties (i.e. with less artificial assumptions for the characterisation of imprecise information). The use of generalised method to quantify uncertainty generally changes the representation of the output of interest from point-valued probabilistic estimators to non-crisp, imprecise (e.g. interval-valued or fuzzy) probabilistic estimators, truthfully representing the available (but incomplete) input data.
Although imprecise probability offers a more powerful representation of the uncertainty, applications of imprecise probability in engineering is still limited. This is often due to the lack of efficient computational tools and simulation techniques.
This talk will present an overview of efficient computation approaches for imprecise probability and their application for solving challenging engineering problems.
Imprecise inferences over Boolean functions made easy
Dr Sebastien Destercke, CNRS/UMR Heudiasyc
In this talk, we will briefly discuss the general problem of making inferences over Boolean functions when the truth values of variables are uncertainly known, and when this uncertainty is described by probability bounds. While such inferences are in general NP-hard to achieve, we will then focus on tractable subcases, illustrating their use in problems such as reliability analysis or short-term trajectory planning for autonomous vehicles (and moving robots in general). If times allow, we will then discuss open problems.
16:00-16:30: Cristian Greco
Robust Particle Filter for Space Navigation under Epistemic Uncertainty
16:30-17:00: Daniel Krpelik
Simultaneous Sampling for Robust Markov Chain Monte Carlo Inference
Imprecise Probability Approaches for the Statistical Modelling of Set-Valued Data
Prof Thomas Augustin, Department of Statistics, LMU Munich
The talk provides an overview of recent research on the statistical modelling of set-valued data. Set-valued data occur naturally in a variety of situations. In particular, they arise from inaccurate measurements, in the analysis of ranges, in the study of partially determined choices or from partial classification.
The choice of the statistical methods applicable is predetermined by two factors: the scale of measurement of the set-valued data and the interpretation of the set-valued character. There, Couso’s and Dubious’ distinction between epistemic imprecision (imprecise observation of something precise) and ontic imprecision (precise observation of something genuinely imprecise) is crucial.
For the continuous case, where set-valued data are typically interval-valued, we present an approach for generalized linear regression models that relies on set-valued solutions of likelihood-based score-functions. We sketch the relationship to some work in robust optimization and the area of partial identification, provide a formulation as a penalized optimization problem under linear constraints and discuss how to defy the curse of dimensionality by using ideas from statistical sufficiency.
For the categorical case, we provide a profile likelihood approach for multinomial regression models under epistemic imprecision, develop regularized choice models under ontic imprecision and finally discuss ideas for alternative modelling strategies and some extensions.
Prof Alessandro Parente
Universite Libre de Bruxelles, Belgium
The use of machine learning algorithms to predict the behaviours of complex systems is booming. However, the key for an effective use of machine learning tools in multi-physics problems, including combustion, is to couple them to physical and computer models, to embody in them all the prior knowledge and physical constraints that can enhance their performances, and to improve them based on the feedback coming for the validation experiments. In other words, we need to adapt the scientific method to bring machine learning into the picture and make the best use of the massive amount of data we have produced thanks to the advances in numerical computing.
The talk reviews some of the open opportunities for the application of data-driven, reduced-order modelling to combustion systems. In particular, the first webinar focuses on dimensionality reduction in the context of reacting flow applications. Different approaches (based on modal decomposition, neural networks, kernel methods) are presented and compared, based on their ability to identify low-dimensional manifold and provide relevant features.
Alessandro Parente got his Master Degree in Chemical Engineering at the Università di Pisa in 2005. He then carried out a PhD at the same University in collaboration with the University of Utah, where he served as Research Associate from November 2007 to December 2009. In April 2009, Prof Parente started working at the von Karman Institute of Fluid Dynamics. In October 2010, he was appointed Assistant Professor at the Aero-Thermo-Mechanical Department of Université Libre de Bruxelles. Since 2019, he is Professor at the same Institution. In January 2015, Prof Parente founded the BURN joint research group on combustion and robust optimization, involving 7 full time professors and around 40 researchers. His research interests are in the field of turbulent/chemistry interaction in turbulent combustion and reduced-order models, non-conventional fuels and pollutant formation in combustion systems, novel combustion technologies, numerical simulation of atmospheric boundary layer flows, and validation and uncertainty quantification. He is author and co-author of more than 75 papers on International Archival Journals.
Wed 18 Nov
From Uncertainty to Shape Optimisation: Cross-Fertilisation of Methods for Dimensionality Reduction
Dr Matteo Diez, CNR-INM, National Research Council-Institute of Marine Engineering
Theory and techniques for stochastic processes and dimensionality reduction in uncertainty modeling have demonstrated their capability of providing a powerful framework for design-space variability assessment and dimensionality reduction in global shape optimization. This cross-fertilization of methods allows for the efficient exploration of large design spaces in shape optimization, which, in turn, enables global multidisciplinary optimization under uncertainty. The talk and paper will review and discuss recent techniques for design-space dimensionality reduction based on the KLE. A historical parallel will be drawn with approaches using POD/PCA. A discussion will be provided on the pros and cons associated to the use of different design variables and metrics, from shape-modification vector components to relevant physical outputs, spanning from geometry-only approaches to physics-informed formulations. Applications will be shown and discussed for hydrodynamic optimization of vessels.
Quantification of operational and geometrical uncertainties of a 1.5 stage axial compressor with cavity leakage flows
Dr Dirk Wunsch, NUMFLO
Simultaneous operational and geometrical uncertainties are
quantified on a 1.5 stage axial compressor (stator-rotor-stator) with leakage
flows. The compressor geometry and operational conditions are provided by
Safran Aero Boosters. Uncertainties include the mass flow rates of the leakage
flows, the tip gap size of the rotor and correlated profiles for the main inflow
conditions. In total 9 uncertainties are propagated by the Non-Intrusive
Probabilistic Collocation method available within FINETM/Design3D.
Uncertainties are quantified for the nominal design point as well as for two
off-design operating points, close to stall and choke conditions respectively.
The non-deterministic response is analyzed and discussed in detail. It is shown
that a sensitivity study, by means of scaled sensitivity derivatives, allows
identifying the most influential uncertainties in the operation of this 1.5 stage
13:00-13:30: Barbara Arizmendi Gutierrez
Uncertainty Quantification of a Thermal Ice Protection System
13:30-14:00: Salvatore Iavarone
Data-driven Incompletely Stirred Reactor Network Modeling of an Aero-Engine Model Combustor
14:00-14:30: Andrés Cacereño
Multi-Objective Optimal Design and Maintenance for Systems Based on Calendar Times Using MOEA/D-DE
14:45-15:15 Thomas Caleb, Stéphanie Lizy-Destrez
Can Uncertainty Propagation Solve the Mysterious Case of Snoopy?
15:15-15:45 Ben Parsonage
A multi-fidelity model management framework for multi-objective aerospace design optimisation
15:45-16:15 Lino Costa, Antonio Gaspar-Cunha
Multi-Objective Robustness Analysis of the Polymer Extrusion Process
16:30-17:00 Elisa Morales Tirado
Multi-fidelity Surrogate Assisted Design Optimisation of an Airfoil under Uncertainty using Far-Field Drag Approximation
17:00-17:30 Riccardo Tosi, Marc Núñez
Scalable dynamic asynchronous Monte Carlo framework applied to wind engineering problems
17:30-18:00 Salvatore Iavarone
Combined effect of experimental and kinetic uncertainties on NO predictions in low-pressure premixed laminar H2/CH4/CO-air flames
Thu 19 Nov
Bayes Linear Approach to UQ and Decision Support with application to Energy Systems
Dr Dario Domingo, Durham University
Bayesian Statistics & Machine Learning
11:00-11:30 Aurelie Bellemans
A Machine-Learning framework for plasma-assisted combustion using Principal Component Analysis and Gaussian Process regression
11:30-12:00 Adam Errington
Estimating exposure fraction from radiation biomarkers: a comparison of frequentist and Bayesian approaches
12:00-12:30 Tathagata Basu
Bayesian Variable selection under prior ignorance
UQ for dynamic models and inverse problems I
13:30-14:00 Krushna Shinde
Dealing with high-dimensional inconsistent measurements in inverse problems using surrogate modelling: an approach based on sets and intervals
14:00-14:30 Giulio Gori
A review of some recent advancements in non-ideal compressible fluid dynamics
UQ for dynamic models and inverse problems II
14:45-15:15 Anabel del Val
Inference methods for gas/surface interaction models: from deterministic approaches to Bayesian methods
15:15-15:45 Jao Reis
Stochastic preconditioners for domain decomposition methods
Explaining the distribution of energy consumption at slow charging infrastructure for electric vehicles from socio-economic data
Prof Rui Carvalho, Durham University
Here, we develop a data-centric approach enabling to analyse which activities, function, and characteristics of the environment surrounding the slow charging infrastructure impact the distribution of the electricity consumed at slow charging infrastructure. To gain a basic insight, we analysed the probabilistic distribution of energy consumption and its relation to indicators characterizing charging events. We collected geospatial datasets and utilizing statistical methods for data pre-processing, we prepared features modelling the spatial context in which the charging infrastructure operates. To enhance the statistical reliability of results, we applied the bootstrap method together with the Lasso method that combines regression with variable selection ability. We evaluate the statistical distributions of the selected regression coefficients. We identified the most influential features correlated with energy consumption, indicating that the spatial context of the charging infrastructure affects its utilization pattern. Many of these features are related to the economic prosperity of residents. Application of the methodology to a specific class of charging infrastructure enables the differentiation of selected features, e.g. by the used rollout strategy. Overall, the application of statistical methodologies to energy data will be discussed, with insights on factors potentially shaping the energy consumption that could be utilized when developing models to inform charging infrastructure deployment and planning of power grids.