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Paper des Monats
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The presentations discusses a use case for the optimization of concrete structures where structural and material design are integrated in a computational workflow. The workflow is based on both physics-based and data-based models and experimental data is used to calibrate/train these models with a specific focus on the integration of ucertainties.
We introduce a novel adaptive Gaussian Process Regression (GPR) methodology for efficient construction of surrogate models for Bayesian inverse problems with expensive forward model evaluations. An adaptive design strategy focuses on optimizing both the positioning and simulation accuracy of training data in order to reduce the computational cost of simulating training data without compromising the fidelity of the posterior distributions of parameters. The method interleaves a goal-oriented active learning algorithm selecting evaluation points and tolerances based on the expected impact on the Kullback-Leibler divergence of surrogated and true posterior with a Markov Chain Monte Carlo sampling of the posterior. The performance benefit of the adaptive approach is demonstrated for two simple test problems.
The development of standards that include simulation models is often based on heuristics that include experimental data that is used to calibrate and validate the simulation models. However, these data and the corresponding calibration procedures are usually not part of the publicly accessible documentation making it very difficult to identify gaps in the current standard or extend it to new innovative solutions. The poster shows an adapted procedure that integrates the complete process into a database and a workflow to be published alongside the standard.
We introduce a novel adaptive Gaussian Process Regression (GPR) methodology for efficient construction of surrogate models for Bayesian inverse problems with expensive forward model evaluations. An adaptive design strategy focuses on optimizing both the positioning and simulation accuracy of training data in order to reduce the computational cost of simulating training data without compromising the fidelity of the posterior distributions of parameters. The method interleaves a goal-oriented active learning algorithm selecting evaluation points and tolerances based on the expected impact on the Kullback-Leibler divergence of surrogated and true posterior with a Markov Chain Monte Carlo sampling of the posterior. The performance benefit of the adaptive approach is demonstrated for two simple test problems.
Thermal transient problems, essential for modeling applications like welding and additive metal manufacturing, are characterized by a dynamic evolution of temperature. Accurately simulating these phenomena is often computationally expensive, thus limiting their applications, for example for model parameter estimation or online process control. Model order reduction, a solution to preserve the accuracy while reducing the computation time, is explored. This article addresses challenges in developing reduced order models using the proper generalized decomposition (PGD) for transient thermal problems with a specific treatment of the moving heat source within the reduced model. Factors affecting accuracy, convergence, and computational cost, such as discretization methods (finite element and finite difference), a dimensionless formulation, the size of the heat source, and the inclusion of material parameters as additional PGD variables are examined across progressively complex examples. The results demonstrate the influence of these factors on the PGD model’s performance and emphasize the importance of their consideration when implementing such models. For thermal example, it is demonstrated that a PGD model with a finite difference discretization in time, a dimensionless representation, a mapping for a moving heat source, and a spatial domain non-separation yields the best approximation to the full order model.
Multiscale modeling of linear elastic heterogeneous structures via localized model order reduction
(2024)
In this paper, a methodology for fine scale modeling of large scale linear elastic structures is proposed, which combines the variational multiscale method, domain decomposition and model order reduction. The influence of the fine scale on the coarse scale is modelled by the use of an additive split of the displacement field, addressing applications without a clear scale separation. Local reduced spaces are constructed bysolving an oversampling problem with random boundary conditions. Herein, we inform the boundary conditions by a global reduced problem and compare our approach using physically meaningful correlated samples with existing approaches using uncorrelated samples. The local spaces are designed such that the local contribution of each subdomain can be coupled in a conforming way, which also preserves the sparsity pattern of standard finite element assembly procedures. Several numerical experiments show the accuracy and efficiency of the method, as well as its potential to reduce the size of the local spaces and the number of training samples compared to the uncorrelated sampling
In the field of computational science and engineering, workflows often entail the application of various software, for instance, for simulation or pre- and postprocessing. Typically, these components have to be combined in arbitrarily complex workflows to address a specific research question. In order for peer researchers to understand, reproduce and (re)use the findings of a scientific publication, several challenges have to be addressed. For instance, the employed workflow has to be automated and information on all used software must be available for a reproduction of the results. Moreover, the results must be traceable and the workflow documented and readable to allow for external verification and greater trust. In this paper, existing workflow management systems (WfMSs) are discussed regarding their suitability for describing, reproducing and reusing scientific workflows. To this end, a set of general requirements for WfMSswere deduced from user stories that we deem relevant in the domain of computational science and engineering. On the basis of an exemplary workflow implementation, publicly hosted at GitHub (https:// this http URL), a selection of different WfMSs is compared with respect to these requirements, to support fellow scientists in identifying the WfMSs that best suit their requirements.
Despite the advances in hardware and software techniques, standard numerical methods fail in providing real-time simulations, especially for complex processes such as additive manufacturing applications. A real-time simulation enables process control through the combination of process monitoring and automated feedback, which increases the flexibility and quality of a process. Typically, before producing a whole additive manufacturing structure, a simplified experiment in the form of a beadon-plate experiment is performed to get a first insight into the process and to set parameters suitably. In this work, a reduced order model for the transient thermal problem of the bead-on-plate weld simulation is developed, allowing an efficient model calibration and control of the process. The proposed approach applies the proper generalized decomposition (PGD) method, a popular model order reduction technique, to decrease the computational effort of each model evaluation required multiple times in parameter estimation, control, and optimization. The welding torch is modeled by a moving heat source, which leads to difficulties separating space and time, a key ingredient in PGD simulations. A novel approach for separating space and time is applied and extended to 3D problems allowing the derivation of an efficient separated representation of the temperature.
The results are verified against a standard finite element model showing excellent agreement. The reduced order model is also leveraged in a Bayesian model parameter estimation setup, speeding up calibrations and ultimately leading to an optimized real-time simulation approach for welding experiment using synthetic as well as real measurement data.
Data provenance - from experimental data to trust worthy simulation models and standards Jörg F. Unger, Annika Robens-Radermacher, Erik Tamsen Bundesanstalt für Materialforschung und -prüfung (BAM). Unter den Eichen 87, 12205 Berlin, Germany FAIR (findable, accessible, interoperable and reusable) data usage is one of the main principals that many of the research and funding organizations include in their strategic plans, which means that following the main principals of FAIR data is required in many research projects. The definition of data being FAIR is very general, and when implementing that for a specific application or project or even setting a standardized procedure within a working group, a company or a research community, many challenges arise. In this contribution, an overview about our experience with different methods, tools and procedures is outlined. We begin with a motivation on potential use cases for the applications of FAIR data with increasing complexity starting from a reproducible research paper over collaborative projects with multiple participants such as Round-Robin tests up to data-based models within standardization codes, applications in machine learning or parameter estimation of physics-based simulation models. In a second part, different options for structuring the data are discussed. On the one hand, this includes a discussion on how to define actual data structures and in particular metadata schema, and on the other hand, two different systems for storing the data are discussed. The first one is the open BIS system, which is an opensource Lab notebook and Postgre SQL based data management system. A second option are a semantic representations using RDF based ontologies for the domain of interest. In a third section, requirements for workflow tools to automate data processing are discussed and their integration into reproducible data analysis is presented with an outlook on required information to be stored as metadata in the database.
FAIR (findable, accessible, interoperable and reusable) data usage is one of the main principals that many of the research and funding organizations include in their strategic plans, which means that following the main principals of FAIR data is required in many research projects. The definition of data being FAIR is very general, and when implementing that for a specific application or project or even setting a standardized procedure within a working group, a company or a research community, many challenges arise. In this contribution, an overview about our experience with different methods, tools and procedures is outlined.
We begin with a motivation on potential use cases for the applications of FAIR data with increasing complexity starting from a reproducible research paper over collaborative projects with multiple participants such as Round-Robin tests up to data-based models within standardization codes, applications in machine learning or parameter estimation of physics-based simulation models.
In a second part, different options for structuring the data are discussed. On the one hand, this includes a discussion on how to define actual data structures and in particular metadata schema, and on the other hand, two different systems for storing the data are discussed. The first one is the openBIS system, which is an open-source Lab notebook and PostgreSQL based data management system. A second option are a semantic representations using RDF based ontologies for the domain of interest.
In a third section, requirements for workflow tools to automate data processing are discussed and their integration into reproducible data analysis is presented with an outlook on required information to be stored as metadata in the database.
Finally, the presented procedures are exemplarily demonstrated for the calibration of a temperature dependent constitutive model for additively manufactured mortar. Metadata schemata for a rheological measurement setup are derived and implemented in an openBIS database. After a short review of a potential numerical model predicting the structural build-up behaviour, the automatic workflow to use the stored data for model parameter estimation is demonstrated.