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2021-11-30T19:23:00+01:00
Modeling Dynamic Systems using Slope Constraints: An Application Analysis of Gas Turbines
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In energy studies, researchers build models for dynamic systems to predict the produced electrical output precisely. Since experiments are expensive, the researchers rely on simulations of surrogate models. These models use differential equations that can provide decent results but are computationally expensive. Further, transition phases, which occur when an input change results in a delayed change in output, are modeled individually and therefore lacking generalizability.
Current research includes Data Science approaches that need large amounts of data, which are costly when performing scientific experiments. Theory-Guided Data Science aims to combine Data Science approaches with domain knowledge to reduce the amount of data needed while predicting the output precisely.
However, even state-of-the-art Theory-Guided Data Science approaches lack the possibility to model the slopes occuring in the transition phases. In this thesis we aim to close this gap by proposing a new loss constraint that represents both transition and stationary phases. Our method is compared with theoretical and Data Science approaches on synthetic and real world data.
Florian Leiser
20
Proposal
2021-02-02T17:16:18Z
2459248.2196528
Modeling Dynamic Systems using Slope Constraints: An Application Analysis of Gas Turbines
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[[:Institutsseminar/2021-02-05 Zusatztermin]]
Modeling Dynamic Systems using Slope Constraints: An Application Analysis of Gas Turbines# QUERYbc8557d20c31cb7589697dfb18521a9c