Evidence map›Paper›PMID 35755874›Full record

ArticlePatterns (New York, N.Y.)2022

Multi-input convolutional network for ultrafast simulation of field evolvement.

Zhuo Wang, Wenhua Yang, Linyan Xiang, Xiao Wang, Yingjie Zhao, Yaohong Xiao, Pengwei Liu, Yucheng Liu, Mihaela Banu, Oleg Zikanov and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Zhuo WangDepartment of Mechanical Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA.
Wenhua YangDepartment of Mechanical Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA.
Linyan XiangDepartment of Mechanical Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA.
Xiao WangSchool of Mechatronic Engineering, Jiangsu Normal University, Xuzhou, Jiangsu 221116, China.
Yingjie ZhaoCollege of Mechanical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China.
Yaohong XiaoDepartment of Mechanical Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA.
Pengwei LiuCollege of Mechanical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China.
Yucheng LiuDepartment of Mechanical Engineering, Mississippi State University, Starkville, MS 39762, USA.
Mihaela BanuDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48128, USA.
Oleg ZikanovDepartment of Mechanical Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA.
Lei ChenDepartment of Mechanical Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is a compelling need for the regression capability of mapping the initial field and applied conditions to the evolved field, e.g., given current flow field and fluid properties predicting next-step flow field. Such a capability can provide a maximum to full substitute of a physics-based model, enabling fast simulation of various field evolvements. We propose a conceptually simple, lightweight, but powerful multi-input convolutional network (ConvNet), yNet, that merges multi-input signals by manipulating high-level encodings of field/image input. yNet can significantly reduce the model size compared with its ConvNet counterpart (e.g., to only one-tenth for main architecture of 38-layer depth) and is as much as six orders of magnitude faster than a physics-based model. yNet is applied for data-driven modeling of fluid dynamics, porosity evolution in sintering, stress field development, and grain growth. It consistently shows great extrapolative prediction beyond training datasets in terms of temporal ranges, spatial domains, and geometrical shapes.

Indexed as

additive manufacturingconvolutional networkdata-driven modelingfluid dynamicsgrain growthmicrostructureporosityscientific machine learningselective laser sinteringstress

Identifiers

PMID35755874
PMCPMC9214322

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.