Efficient data structures for model-free data-driven computational mechanics

The data-driven computing paradigm initially introduced by Kirchdoerfer & Ortiz (2016) enables finite element computations in solid mechanics to be performed directly from material data sets, without an explicit material model. From a computational effort point of view, the most challenging task is the projection of admissible states at material points onto their closest states in the material data set. In this study, we compare and develop several possible data structures for solving the nearest-neighbor problem. We show that approximate nearest-neighbor (ANN) algorithms can accelerate material data searches by several orders of magnitude relative to exact searching algorithms. The approximations are suggested by—and adapted to—the structure of the data-driven iterative solver and result in no significant loss of solution accuracy. We assess the performance of the ANN algorithm with respect to material data set size with the aid of a 3D elasticity test case. We show that computations on a single processor with up to one billion material data points are feasible within a few seconds execution time with a speed up of more than 10⁶ with respect to exact k-d trees

Model-Free Data-Driven Inelasticity

We extend the Data-Driven formulation of problems in elasticity of Kirchdoerfer and Ortiz (2016) to inelasticity. This extension differs fundamentally from Data-Driven problems in elasticity in that the material data set evolves in time as a consequence of the history dependence of the material. We investigate three representational paradigms for the evolving material data sets: i) materials with memory, i.e., conditioning the material data set to the past history of deformation; ii) differential materials, i.e., conditioning the material data set to short histories of stress and strain; and iii) history variables, i.e., conditioning the material data set to ad hoc variables encoding partial information about the history of stress and strain. We also consider combinations of the three paradigms thereof and investigate their ability to represent the evolving data sets of different classes of inelastic materials, including viscoelasticity, viscoplasticity and plasticity. We present selected numerical examples that demonstrate the range and scope of Data-Driven inelasticity and the numerical performance of implementations thereof.Comment: Minor revisions: affiliations, acknowledgment

Gradient-extended brittle damage modeling

An elastic-brittle anisotropic model is presented based on the work by Fassin et al. (2019a). After discussing the local model equations and the incorporation of crack-closure, the gradient extension using the micromorphic approach according to Forest (2009) is briefly summarized. In order to run unit cell simulations on the microlevel, relevant material parameters have to be identified. Therefore, the energy dissipation provides a differential equation with a linear and quadratic term for the damage variable. Finally, the isotropic damage model is used to show numerical examples with variation of fracture toughness and volume fraction of pores

Assessing the Impact of Misclassification Error on an Epidemiological Association between Two Helminthic Infections

Hookworm, roundworm, and whipworm are collectively known as soil-transmitted helminths. These worms are prevalent in most of the developing countries along with another parasitic infection called schistosomiasis. The tests commonly used to detect infection with these worms are less than 100% accurate. This leads to misclassification of infection status since these tests cannot always correctly indentify infection. We conducted an epidemiological study where such a test, the Kato-Katz technique, was used. In our study we tried to show how misclassification error can influence the association between soil-transmitted helminth infection and schistosomiasis in humans. We used a statistical technique to calculate epidemiological measures of association after correcting for the inaccuracy of the test. Our results show that there is a major difference between epidemiological measures of association before and after the correction of the inaccuracy of the test. After correction of the inaccuracy of the test, soil-transmitted helminth infection was found to be associated with increased risk of acquiring schistosomiasis. This has major public health implications since effective control of one worm can lead to reduction in the occurrence of another and help to reduce the overall burden of worm infection in affected regions

Model-Free Data-Driven inelasticity

We extend the Data-Driven formulation of problems in elasticity of Kirchdoerfer and Ortiz (2016) to inelasticity. This extension differs fundamentally from Data-Driven problems in elasticity in that the material data set evolves in time as a consequence of the history dependence of the material. We investigate three representational paradigms for the evolving material data sets: (i) materials with memory, i. e., conditioning the material data set to the past history of deformation; (ii) differential materials, i. e., conditioning the material data set to short histories of stress and strain; and (iii) history variables, i. e., conditioning the material data set to ad hoc variables encoding partial information about the history of stress and strain. We also consider combinations of the three paradigms thereof and investigate their ability to represent the evolving data sets of different classes of inelastic materials, including viscoelasticity, viscoplasticity and plasticity. We present selected numerical examples that demonstrate the range and scope of Data-Driven inelasticity and the numerical performance of implementations thereof

ToSkORL: Selbst- und Fremdeinschätzung bei der Untersuchung des Kopf-Hals-Bereichs

BACKGROUND A~central goal of medical school is acquisition of theoretical and practical competences. However, evidence on how capacity acquisition can be measured for special examination techniques is scarce. ToSkORL (Teaching of Skills in Otorhinolaryngology) is a project aimed at scientifically and didactically investigating students' self-evaluation skills in otorhinolaryngologic and head and neck examination techniques. METHODS During the examination techniques course, a~standardized oral and practical exam for nine different techniques was conducted. Using Likert scales, self-evaluation was based on questionnaires before the clinical skills exam and objective evaluation was performed by the examiners during the examination using a checklist. Self- and objective evaluation were correlated. Nine different examination skills were assessed 42~times each by a~total of 91~students. RESULTS Self-evaluation of competence in the different examination skills varied widely. Nevertheless, self- and objective evaluation correlated well overall, independent of age and gender. Students highly interested in otorhinolaryngology rated their own skills higher but tended toward overestimation. For examination items with intermediate difficulty, the highest divergences between self- and objective evaluation were found. CONCLUSION Student self-evaluations are an appropriate instrument for measuring competences in otorhinolaryngologic examinations. Instructors should focus on items with allegedly intermediate difficulty, which are most often over- and underestimated. ZUSAMMENFASSUNG HINTERGRUND: Ein zentrales Ziel des Medizinstudiums ist der Erwerb theoretischer und praktischer Kompetenzen. Es mangelt jedoch an Evidenz, wie der Erwerb von Kompetenzen in speziellen Untersuchungstechniken gemessen werden kann. ToSkORL (Teaching of Skills in Otorhinolaryngology) ist ein Projekt, das die studentische Selbstwahrnehmung ihrer Kompetenz bei speziellen Untersuchungstechniken der Hals-Nasen-Ohren-Heilkunde und des Kopf-Hals-Bereichs aus didaktisch-wissenschaftlicher Sichtweise beleuchtet. METHODIK Im Rahmen des Untersuchungskurses erfolgte eine standardisierte mündlich-praktische Prüfung zu neun verschiedenen Untersuchungstechniken. Vor der Prüfung erfolgte eine Evaluation der studentischen Selbsteinschätzung mittels Fragebogen, die Prüfung wurde mittels Checkliste durch die Prüfenden standardisiert geprüft. Selbst- und Fremdeinschätzung nach der Likert-Skala wurden korreliert. Die neun Untersuchungstechniken wurden jeweils 42-mal von insgesamt 91~Studierenden in gegenseitiger Untersuchung durchgeführt. ERGEBNISSE Die Selbsteinschätzung der Kompetenz in den Untersuchungstechniken variiert erheblich, insgesamt schätzten Studierende ihre eigene Untersuchungskompetenz weitgehend unabhängig von Alter und Geschlecht meist realistisch ein. Studierende mit einem hohen Interesse an der Hals-Nasen-Ohren-Heilkunde gaben bessere Selbsteinschätzungen an, neigten jedoch auch eher zur Selbstüberschätzung. Bei Untersuchungen des mittleren Schwierigkeitsniveaus ergab sich die größte Divergenz von Selbst- und Fremdeinschätzung. SCHLUSSFOLGERUNG Die studentische Selbsteinschätzung ist ein geeignetes Instrument zur Messung der Untersuchungskompetenz in der Hals-Nasen-Ohren-Heilkunde. Es sollte ein besonderer Fokus auf die Lehre vermeintlich mittelschwerer Untersuchungstechniken gelegt werden, da diese am stärksten über- und unterschätzt werden