Shallow neural networks to predict glass transition, crystallization and liquidus temperature of iron-based metallic glasses

Titel:	Shallow neural networks to predict glass transition, crystallization and liquidus temperature of iron-based metallic glasses / Kirsten Bobzin, Wolfgang Wietheger, Lukas M. Johann
Verfasser:	Bobzin, Kirsten ; Wietheger, Wolfgang ; Johann, Lukas M.
Veröffentlicht:	Aachen : Universitätsbibliothek der RWTH Aachen, 2021
Umfang:	1 Online-Ressource
Format:	E-Book
Sprache:	Englisch

alg: 48151097
001A    $06000:08-07-21 
001B    $01999:09-07-21 $t22:54:54.000 
001D    $06000:08-07-21 
001U    $0utf8 
001X    $00 
002@    $0Oax 
002C    $aText $btxt $2rdacontent 
002D    $aComputermedien $bc $2rdamedia 
002E    $aOnline-Ressource $bcr $2rdacarrier 
003@    $0481510974 
004P    $010.18154/RWTH-2021-05342 
004U    $0urn:nbn:de:101:1-2021061004400708672211 
006G    $01235142558 
006U    $021,O07 $f20210610 
007A    $01235142558 $aDNB 
009Q    $uhttps://doi.org/10.18154/RWTH-2021-05342 $xR 
009Q    $uhttps://nbn-resolving.org/urn:nbn:de:101:1-2021061004400708672211 $xR 
009Q    $qapplication/pdf $S0 $uhttps://publications.rwth-aachen.de/record/819953 $xH 
010@    $aeng 
011@    $a2021 $n2021 
021A    $aShallow neural networks to predict glass transition, crystallization and liquidus temperature of iron-based metallic glasses $hKirsten Bobzin, Wolfgang Wietheger, Lukas M. Johann 
028A    $BVerfasser $#Bobzin, K. $4aut $7140759026 $8Bobzin, Kirsten$Z1967- [Tp3] $9221567550 
028C    $aWietheger $BVerfasser $dWolfgang $4aut 
028C    $aJohann $BVerfasser $dLukas M. $4aut 
033A    $pAachen $nUniversitätsbibliothek der RWTH Aachen 
034D    $a1 Online-Ressource 
037A    $aIn: 10.1088/1757-899X/1147/1/012012 
045E    $c320 
046X    $aArchivierung/Langzeitarchivierung gewährleistet $5DE-101 

lok: 48151097 3

exp: 48151097 3 1 #EPN
201B/01 $009-07-21 $t22:54:54.466 
201C/01 $009-07-21 
201U/01 $0utf8 
203@/01 $01161516344 
208@/01 $a09-07-21 $bl 
209S/01 $S0 $uhttps://publications.rwth-aachen.de/record/819953 $XH 

lok: 48151097 5

exp: 48151097 5 1 #EPN
201B/01 $009-07-21 $t22:54:54.473 
201C/01 $009-07-21 
201U/01 $0utf8 
203@/01 $01161516352 
208@/01 $a09-07-21 $bl 
209S/01 $S0 $uhttps://publications.rwth-aachen.de/record/819953 $XH 

lok: 48151097 8

exp: 48151097 8 1 #EPN
201B/01 $009-07-21 $t22:54:54.479 
201C/01 $009-07-21 
201U/01 $0utf8 
203@/01 $01161516360 
208@/01 $a09-07-21 $bl 
209S/01 $S0 $uhttps://publications.rwth-aachen.de/record/819953 $XH 

lok: 48151097 10

exp: 48151097 10 1 #EPN
201B/01 $009-07-21 $t22:54:54.486 
201C/01 $009-07-21 
201U/01 $0utf8 
203@/01 $01161516379 
208@/01 $a09-07-21 $bl 
209S/01 $S0 $uhttps://publications.rwth-aachen.de/record/819953 $XH 

lok: 48151097 11

exp: 48151097 11 1 #EPN
201B/01 $009-07-21 $t22:54:54.492 
201C/01 $009-07-21 
201U/01 $0utf8 
203@/01 $01161516387 
208@/01 $a09-07-21 $bl 
209S/01 $S0 $uhttps://publications.rwth-aachen.de/record/819953 $XH 

lok: 48151097 20

exp: 48151097 20 1 #EPN
201B/01 $009-07-21 $t22:54:54.498 
201C/01 $009-07-21 
201U/01 $0utf8 
203@/01 $01161516395 
208@/01 $a09-07-21 $bl 
209S/01 $S0 $uhttps://publications.rwth-aachen.de/record/819953 $XH 

lok: 48151097 21

exp: 48151097 21 1 #EPN
201B/01 $009-07-21 $t22:54:54.504 
201C/01 $009-07-21 
201U/01 $0utf8 
203@/01 $01161516409 
208@/01 $a09-07-21 $bl 
209S/01 $S0 $uhttps://publications.rwth-aachen.de/record/819953 $XH 

lok: 48151097 23

exp: 48151097 23 1 #EPN
201B/01 $009-07-21 $t22:54:54.511 
201C/01 $009-07-21 
201U/01 $0utf8 
203@/01 $01161516417 
208@/01 $a09-07-21 $bl 
209S/01 $S0 $uhttps://publications.rwth-aachen.de/record/819953 $XH 

lok: 48151097 24

exp: 48151097 24 1 #EPN
201B/01 $009-07-21 $t22:54:54.517 
201C/01 $009-07-21 
201U/01 $0utf8 
203@/01 $01161516425 
208@/01 $a09-07-21 $bl 
209S/01 $S0 $uhttps://publications.rwth-aachen.de/record/819953 $XH 

lok: 48151097 25

exp: 48151097 25 1 #EPN
201B/01 $009-07-21 $t22:54:54.523 
201C/01 $009-07-21 
201U/01 $0utf8 
203@/01 $01161516433 
208@/01 $a09-07-21 $bl 
209S/01 $S0 $uhttps://publications.rwth-aachen.de/record/819953 $XH 

lok: 48151097 36

exp: 48151097 36 1 #EPN
201B/01 $009-07-21 $t22:54:54.529 
201C/01 $009-07-21 
201U/01 $0utf8 
203@/01 $01161516441 
208@/01 $a09-07-21 $bl 
209S/01 $S0 $uhttps://publications.rwth-aachen.de/record/819953 $XH 

lok: 48151097 49

exp: 48151097 49 1 #EPN
201B/01 $009-07-21 $t22:54:54.534 
201C/01 $009-07-21 
201U/01 $0utf8 
203@/01 $0116151645X 
208@/01 $a09-07-21 $bl 
209S/01 $S0 $uhttps://publications.rwth-aachen.de/record/819953 $XH 

lok: 48151097 54

exp: 48151097 54 1 #EPN
201B/01 $009-07-21 $t22:54:54.547 
201C/01 $009-07-21 
201U/01 $0utf8 
203@/01 $01161516476 
208@/01 $a09-07-21 $bl 
209S/01 $S0 $uhttps://publications.rwth-aachen.de/record/819953 $XH 

lok: 48151097 108

exp: 48151097 108 1 #EPN
201B/01 $009-07-21 $t22:54:54.558 
201C/01 $009-07-21 
201U/01 $0utf8 
203@/01 $01161516492 
208@/01 $a09-07-21 $bl 
209S/01 $S0 $uhttps://publications.rwth-aachen.de/record/819953 $XH 

lok: 48151097 204

exp: 48151097 204 1 #EPN
201B/01 $009-07-21 $t22:54:54.564 
201C/01 $009-07-21 
201U/01 $0utf8 
203@/01 $01161516506 
208@/01 $a09-07-21 $bl 
209S/01 $S0 $uhttps://publications.rwth-aachen.de/record/819953 $XH 

lok: 48151097 205

exp: 48151097 205 1 #EPN
201B/01 $009-07-21 $t22:54:54.570 
201C/01 $009-07-21 
201U/01 $0utf8 
203@/01 $01161516514 
208@/01 $a09-07-21 $bl 
209S/01 $S0 $uhttps://publications.rwth-aachen.de/record/819953 $XH

LEADER	00000cam a22000002c 4500
001	481510974
003	DE-603
005	20210709225454.0
007	cr\|\|\|\|\|\|\|\|\|\|\|\|
008	210708s2021 xx \|\|\|\| o\| u00\|\|u\|eng
015			\|a 21,O07 \|2 dnb
016	7		\|2 DE-101 \|a 1235142558
024	7		\|a 10.18154/RWTH-2021-05342 \|2 doi
024	7		\|a urn:nbn:de:101:1-2021061004400708672211 \|2 urn
035			\|a (DE-599)DNB1235142558
040			\|a DE-603 \|b ger \|c DE-603 \|d DE-603
041			\|a eng
084			\|a 320 \|q DE-101 \|2 sdnb
100	1		\|a Bobzin, Kirsten \|d 1967- \|e Verfasser \|4 aut \|0 (DE-603)221567550 \|0 (DE-588)140759026 \|2 gnd
245	0	0	\|a Shallow neural networks to predict glass transition, crystallization and liquidus temperature of iron-based metallic glasses \|c Kirsten Bobzin, Wolfgang Wietheger, Lukas M. Johann
264		1	\|a Aachen \|b Universitätsbibliothek der RWTH Aachen \|c 2021
300			\|a 1 Online-Ressource
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
500			\|a In: 10.1088/1757-899X/1147/1/012012
700	1		\|a Wietheger, Wolfgang \|e Verfasser \|4 aut
700	1		\|a Johann, Lukas M. \|e Verfasser \|4 aut
856			\|u https://doi.org/10.18154/RWTH-2021-05342 \|x Resolving-System
856			\|u https://nbn-resolving.org/urn:nbn:de:101:1-2021061004400708672211 \|x Resolving-System
856			\|q application/pdf \|u https://publications.rwth-aachen.de/record/819953 \|x Verlag \|z kostenfrei
924	1		\|9 603 \|a (DE-603)1161516344 \|b DE-603 \|c HES \|d d
924	1		\|9 603 \|a (DE-603)1161516352 \|b DE-603 \|c HES \|d d
924	1		\|9 603 \|a (DE-603)1161516360 \|b DE-603 \|c HES \|d d
924	1		\|9 603 \|a (DE-603)1161516379 \|b DE-603 \|c HES \|d d
924	1		\|9 603 \|a (DE-603)1161516387 \|b DE-603 \|c HES \|d d
924	1		\|9 603 \|a (DE-603)1161516395 \|b DE-603 \|c HES \|d d
924	1		\|9 603 \|a (DE-603)1161516409 \|b DE-603 \|c HES \|d d
924	1		\|9 603 \|a (DE-603)1161516417 \|b DE-603 \|c HES \|d d
924	1		\|9 603 \|a (DE-603)1161516425 \|b DE-603 \|c HES \|d d
924	1		\|9 603 \|a (DE-603)1161516433 \|b DE-603 \|c HES \|d d
924	1		\|9 603 \|a (DE-603)1161516441 \|b DE-603 \|c HES \|d d
924	1		\|9 603 \|a (DE-603)116151645X \|b DE-603 \|c HES \|d d
924	1		\|9 603 \|a (DE-603)1161516476 \|b DE-603 \|c HES \|d d
924	1		\|9 603 \|a (DE-603)1161516492 \|b DE-603 \|c HES \|d d
924	1		\|9 603 \|a (DE-603)1161516506 \|b DE-603 \|c HES \|d d
924	1		\|9 603 \|a (DE-603)1161516514 \|b DE-603 \|c HES \|d d

Shallow neural networks to predict glass transition, crystallization and liquidus temperature of iron-based metallic glasses

Links

Folgen Sie uns auf...