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DTSTART;TZID=Europe/Berlin:20220428T150000
DTEND;TZID=Europe/Berlin:20220428T160000
DTSTAMP:20230203T092418Z
CREATED:20230119T075829Z
LAST-MODIFIED:20230203T092418Z
UID:465-1651158000-1651161600@www.hsu-hh.de
SUMMARY:Seminar Series: Computation & Data
DESCRIPTION:“Infrastructure Information Modelling: Workflow for an overall model infrastructure embedded in spatial base data” \n\n\n\nJens Bartnitzek (A+S Consult GmbH) \n\n\n\nInfrastructure Information Modelling is an approach which requires rethinking in the sense of cultural shift towards a systematic and partnership-based approach to major projects. Essentials from mathematics\, computer science and civil engineering are necessary for describing the workflow reasonable. The seminar shows in a real world example the process of creating a 3D overall model of an infrastructure facility starting with federal spatial base data. We take a deeper look on the relevant data interfaces and technical workflows. After the inventory model is constructed\, we connect different data from specialized models into the overall model for interdisciplinary use cases. The overall model is for a collaborative “3D planning” with complete information linkage. We show how those use cases can be realized over multiple objects\, connecting multiple data formats based on results from multiple software products.
URL:https://www.hsu-hh.de/hpccp/event/cd2204/
LOCATION:H1\, Hörsaal 3\, Holstenhofweg 85\, Hamburg\, 22043\, Deutschland
CATEGORIES:Seminar Computation & Data
ORGANIZER;CN="hpc.bw":MAILTO:info-hpc-bw@hsu-hh.de
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DTSTART;TZID=Europe/Berlin:20220331T150000
DTEND;TZID=Europe/Berlin:20220331T160000
DTSTAMP:20230203T092421Z
CREATED:20230119T074938Z
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UID:451-1648738800-1648742400@www.hsu-hh.de
SUMMARY:Seminar Series: Computation & Data
DESCRIPTION:15:00-15:30 Yannis Schumann (HSU): Data-driven Inference of Stencils for Discrete Differential Operators \n\n\n\nPartial differential equations (PDEs) are extensively used across scientific disciplines for modeling and describing various processes under consideration. Finite element\, finite difference or similar methods allow to numerically solve them by discretizing the variables under consideration\, e.g. space and time. In this discretized domain\, differential operators can be approximated by matrices with nonzero coefficients for positions in the neighborhood of the considered point –the so called stencil. \n\n\n\nWe consider the inference of stencil weights for differential operators from linear\, one-dimensional and two-dimensional PDEs using comprehensive regression techniques. Starting with the 1D case\, we show that linear regression using an ordinary-least-squares (OLS) approach is able to recover mathematically meaningful stencils given a full-rank matrix of predictor variables. We discuss\, how regularization techniques can allow the inference of the correct stencils even for rank-deficient matrices. We discuss the impact of noise on the data in both predictor and predicted variables for various noise levels and compare different errors-in-variables approaches to mitigate the inherent consequences of noisy predictors. The presented techniques will be extended to the two dimensional case and applied to problems from physics and hydrogeology. \n\n\n\n\n\n\n\n15:30-16:00 Henrik Steude (HSU): It’s more than a “model.train()” — Modern tools and architectures for ML systems \n\n\n\nThe code to train machine learning (ML) models only covers a small part of the entire complexity required by a production-ready ML system. In particular\, model and data versioning\, reproducibility and scalability represent grand challenges. To address these challenges\, various new tools and technologies have been developed under the umbrella term “ML-Ops” over the last years. \n\n\n\nIn our contribution\, we present a selection of technologies\, that have evolved to be popular in the rapidly developing field of ML-Ops. As a concrete example\, we further present a system architecture for a ML platform\, which is used in the dtec.bw project (K)ISS for ML-based analysis of telemetry data of the international space station.
URL:https://www.hsu-hh.de/hpccp/event/cd2203/
LOCATION:H1\, Hörsaal 3\, Holstenhofweg 85\, Hamburg\, 22043\, Deutschland
CATEGORIES:Seminar Computation & Data
ORGANIZER;CN="hpc.bw":MAILTO:info-hpc-bw@hsu-hh.de
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