ST-21 - Session programs - SCEJ 53rd Autumn Meeting (Nagano, 2022)

`SCEJ 53rd Autumn Meeting (Nagano, 2022)`

Last modified: 2023-05-16 07:24:56

Meeting Info.
Program
- Program Menu
- Tech. Sessions
- narrow view
- Day 0
- Day 1
- Day 2
- Day 3
By Session
- Session list
- Ceremony
  - Ceremony
- SV: Vison Symp.
  - SV-1
- SP: Special Symp.
  - SP-1
  - SP-2
- HQ: Hq.,etc.
  - HQ-11
  - HQ-12
  - HQ-13
  - HQ-14
- ST: Trans-div
  - ST-21
  - ST-22
  - ST-23
  - ST-24
  - ST-25
  - ST-26
  - ST-27
  - ST-28
  - ST-29
  - ST-30
- SY: 51-59
  - SY-51
  - SY-52
  - SY-53
  - SY-54
  - SY-55
  - SY-56
  - SY-57
  - SY-58
  - SY-59
- SY: 60-69
  - SY-60
  - SY-61
  - SY-62
  - SY-63
  - SY-64
  - SY-65
  - SY-66
  - SY-67
  - SY-68
  - SY-69
- SY: 70-79
  - SY-70
  - SY-71
  - SY-72
  - SY-73
  - SY-74
  - SY-75
  - SY-76
  - SY-77
  - SY-78
  - SY-79
- SY: 80-83
  - SY-80
  - SY-81
  - SY-82
  - SY-83
  - SY-84
By Hall
- Hall list
- AA-AC: W2
- BA-BB: W5
  - BA W5-21
  - BB W5-23
- CA-CB: W1
  - CA W1-115
  - CB W1-215
- DA-DJ: C3
  - DA C3-100
  - DB C3-101
  - DC C3-102
  - DD C3-103
  - DG C3-202
  - DH C3-203
  - DI C3-300
  - DJ C3-301
- EA: E1 SASTec
  - EA E1 3F
- FA: E2 AICS
  - FA E2 2F
- GA: E6 UFO-Nagano
  - GA E6 5F
- PA-PC: Poster
  - PA
  - PB
  - PC
Search/Index

Session programs : ST-21

The preprints can be viewed by clicking the Paper IDs.
Yellow-back sessions on Technical sessions will be On-site/online Hybrid sessions, others are Online sessions.
Please check notices on menu page.

ST-21 [Trans-Division Symposium]
Frontiers of Data-driven Research and Development

Organizers:

Shimada Iori (Shinshu Univ.), Kim Sanghong (Tokyo Univ. of Agri. and Tech.), Toya Yoshihiro (Osaka Univ.), Kaneko Shogo (Sumitomo Chemical), Mukaida Shiho (Mitsui Chemicals)

Data science has been rapidly developing in recent years as the fourth science following experimental science, theoretical science, and computational science. The early realization of a data-driven society led by data science has been recognized as a key to international competitiveness. This symposium will have speakers who are making pioneering efforts toward a data-driven society from various viewpoints and discuss future research and development.

Hall DC, Day 3

Time	Paper ID	Title / Authors	Keywords	Topic code	Ack. number
Hall DC(C3 1F 102), Day 3(Sep. 16)
(9:00–10:20) (Chair: Mukaida Shiho)
9:00– 9:40	DC301	[Invited lecture] Data-driven AI Laboratory and Cyber Catalysis (Shinshu U.) (Reg)Koyama Michihisa	Data-driven Cyber Catalysis Computational Chemistry	ST-21	102
9:40– 10:20	DC303	[Invited lecture] AI-Driven peptide/antibody molecule design for drug discovery (MOLCURE) Tamaki Satoshi	Artificial Intelligence Drug Discovery Antibody	ST-21	316
(10:20–12:00) (Chair: Toya Yoshihiro)
10:20– 10:40	DC305	Data-driven analysis of charge variants in monoclonal antibody production (U. Tokyo) *(Stu)Yoshiyama Yuki, (Int)Badr Sara, (Stu)Okamura Kozue, (Manufacturing Tech. Association of Biologics) Murakami Sei, (U. Tokyo) (Reg)Sugiyama Hirokazu	Charge variant Monoclonal antibody PLS	ST-21	641
10:40– 11:00	DC306	Multi-step approach for data-driven equipment condition assessment in biopharmaceutical drug product manufacturing (U. Tokyo) *(Int)Zuercher Philipp Samuel, (Int)Badr Sara, (ROCHE) Knueppel Stephanie, (U. Tokyo) (Reg)Sugiyama Hirokazu	Predictive maintenance Unsupervised learning Industrial application	ST-21	590
11:00– 11:20	DC307	Reinforcement learning to optimally control the bio and chemical processes (Kyoto U.) (Int)Oh Tae Hoon	Reinforcement Learning Process control Optimal control	ST-21	60
11:20– 11:40	DC308	Soft sensor study in film manufacturing process (Meiji U.) *(Stu)Nakayama Yuki, Shiraki Yuya, (Zeon) (Cor)Natori Satoshi, (Cor)Ono Yuki, (Cor)Suda Kazuya, (Meiji U.) (Reg)Kaneko Hiromasa	Soft sensor Fault detection Film manufacturing process	ST-21	366
11:40– 12:00	DC309	Prediction of phase equilibrium of water-organic compounds system at high-temperature and high-pressure using machine learning (Shinshu U.) *(Stu)Tamura Kotaro, (Reg)Shimada Iori, (Reg)Fukunaga Hiroshi, (Reg)Takahashi Nobuhide, (Reg)Osada Mitsumasa	machine learning prediction of phase equilibrium high-temperature and high-pressure	ST-21	532
(13:00–14:20) (Chair: Kaneko Shogo)
13:00– 13:40	DC313	[Invited lecture] Exploration of functional inorganic thin-film materials using autonomous systems (Tokyo Tech) Shimizu Ryota	autonomous synthesis inorganic materials functional thin films	ST-21	103
13:40– 14:20	DC315	[Invited lecture] Data-driven polymer material development powered by Polymer SmartLab and Material DX (NIMS) Naito Masanobu	smart lab material DX database	ST-21	129
(14:20–15:40) (Chair: Shimada Iori)
14:20– 14:40	DC317	Inverse design of polymer membrane structure for gas separation using Junction Tree VAE machine learning (Kogakuin U.) *(Stu)Matsumoto Takumi, (Reg)Miyagawa Masaya, (Reg)Takaba Hiromitsu	machine learning polymer membrane gas separation	ST-21	712
14:40– 15:00	DC318	Design of both membrane-based process and membrane materials with machine learning (Meiji U.) *(Stu)Yuyama Shunsuke, (Reg)Kaneko Hiromasa	Membrane module Materials Informatics Process design	ST-21	187
15:00– 15:20	DC319	[Featured presentation] Development of digital twin of the bulk single crystal growth of Si by using PINNs (Physics Informed Neural Networks) (Osaka U.) *(Stu)Takehara Yuto, (Reg)Okano Yasunori	Digital twin Machine learning Physics Informed Neural Networks	ST-21	396
15:20– 15:40	DC320	Growth interface shape optimization and adaptive process control for InGaSb crystal growth under microgravity using machine learning (Osaka U.) *(Stu)Ghritli Rachid, (JAXA-SOKENDAI) Inatomi Yuko, (Osaka U.) (Reg)Okano Yasunori	Machine Learning Reinforcement Learning Crystal Growth	ST-21	428
(15:40–17:00) (Chair: Kim Sanghong)
15:40– 16:00	DC321	Multimodal Artificial Intelligence for Data-driven Developments of Complex Composite Materials (AIST) *(Reg)Muroga Shun, Miki Yasuaki, (ADMAT) Honda Takashi, (AIST) Morita Hiroshi, Okazaki Toshiya, Hata Kenji	Multimodal AI Materials Informatics Composite Material	ST-21	669
16:00– 16:20	DC322	Effect of physics-based feature engineering in predicting product yields of catalytic cracking reactions (Shinshu U.) *(Stu)Yasuike Shun, (Reg)Osada Mitsumasa, (Reg)Shimada Iori	catalytic cracking machine learning feature engineering	ST-21	101
16:20– 16:40	DC323	Developing identifiers to link materials databases (UTokyo) *(Reg)Muraoka Koki, Munekata Tsubasa, Nakayama Akira	materials informatics database	ST-21	580
16:40– 17:00	DC324	Discusstion on initial sample selection for Bayesian optimization of compound combinations (Meiji U.) *(Stu)Morishita Toshiharu, (Reg)Kaneko Hiromasa	Bayesian optimization Machine learning Clustering	ST-21	328

Technical program
Technical sessions (Wide) (For narrow screen)
Session programs
Search in technical program
SCEJ 53rd Autumn Meeting (Nagano, 2022)

www3.scej.org

Session list

HQ-14(How Young Researcher) ST-22(New Developments of )

SCEJ 53rd Autumn Meeting (Nagano, 2022)

Session programs : ST-21

ST-21 [Trans-Division Symposium] Frontiers of Data-driven Research and Development

`SCEJ 53rd Autumn Meeting (Nagano, 2022)`

ST-21 [Trans-Division Symposium]
Frontiers of Data-driven Research and Development