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| 001 | 890024 | ||
| 005 | 20210127115426.0 | ||
| 024 | 7 | _ | |a 2128/26999 |2 Handle |
| 037 | _ | _ | |a FZJ-2021-00618 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Lauer, Patrick |0 P:(DE-HGF)0 |b 0 |
| 111 | 2 | _ | |a Fire and Evacuation Modelling Technical Conference 2020 |g FEMTC 2020 |c virtual |d 2020-09-09 - 2020-09-11 |w virtual |
| 245 | _ | _ | |a Role Of The Cost Function For Material Parameter Determination |
| 260 | _ | _ | |c 2020 |
| 300 | _ | _ | |a 12 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1611568123_25505 |2 PUB:(DE-HGF) |
| 520 | _ | _ | |a Cost functions in optimisation processes are used as a measure to compute the distance between two data sets. Commonly, the root mean square error is used as a cost function for the estimation of material parameters based on bench-scale experiments. Due to the noise and variance in the target experimental data, this may not be the best choice. This contribution presents three other approaches, which are implemented into the PROPTI framework. Their application is demonstrated on experiments with a PMMA sample in a controlled atmosphere pyrolysis apparatus (CAPA II). Although, in the specific case investigated here, the impact of the various cost function classes is small, a benefit is expected for other samples with varying complexity. |
| 536 | _ | _ | |a 511 - Computational Science and Mathematical Methods (POF3-511) |0 G:(DE-HGF)POF3-511 |c POF3-511 |f POF III |x 0 |
| 536 | _ | _ | |a PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405) |0 G:(DE-Juel1)PHD-NO-GRANT-20170405 |c PHD-NO-GRANT-20170405 |x 1 |
| 700 | 1 | _ | |a Hehnen, Tristan |0 P:(DE-Juel1)174283 |b 1 |
| 700 | 1 | _ | |a Trettin, Corinna |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Brännström, Fabian |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Arnold, Lukas |0 P:(DE-Juel1)132044 |b 4 |e Corresponding author |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/890024/files/2020_d3-13-lauer-paper.pdf |y OpenAccess |
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| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)132044 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Supercomputing & Big Data |1 G:(DE-HGF)POF3-510 |0 G:(DE-HGF)POF3-511 |3 G:(DE-HGF)POF3 |2 G:(DE-HGF)POF3-500 |4 G:(DE-HGF)POF |v Computational Science and Mathematical Methods |x 0 |
| 914 | 1 | _ | |y 2020 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-7-20180321 |k IAS-7 |l Zivile Sicherheitsforschung |x 0 |
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