AI physical simulation method and system for calculating differential operator on unstructured grid

The invention relates to an AI physical simulation method and system for calculating a differential operator on an unstructured grid, and the method comprises the steps: carrying out the padding of input initial condition data, and constructing a sparse unstructured grid; calculating a differential...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: SUN HAO, YU FAN, ZENG BOCHENG, ZHANG YI, CHENG ZERUIZHI, LIU HONGSHENG
Format: Patent
Sprache:chi ; eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator SUN HAO
YU FAN
ZENG BOCHENG
ZHANG YI
CHENG ZERUIZHI
LIU HONGSHENG
description The invention relates to an AI physical simulation method and system for calculating a differential operator on an unstructured grid, and the method comprises the steps: carrying out the padding of input initial condition data, and constructing a sparse unstructured grid; calculating a differential operator item on the unstructured grid, embedding the differential operator item in the equation into a network structure, and predicting a system state quantity under the unstructured coarse grid by adopting a message passing graph neural network; and embedding the boundary condition of the equation into the simulation model by adopting a mode of simultaneously padding in the hidden space, so that the simulation model always meets the boundary condition, and AI physical simulation is carried out on the fluid. According to the method, under the condition that sparse training samples are used, the simulation precision of the complex physical field on the non-structural coarse grid is remarkably improved. 本发明涉及一种在非结构
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN118586182A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN118586182A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN118586182A3</originalsourceid><addsrcrecordid>eNqNyz0KAjEQQOE0FqLeYfYAFlGUbZdF0cbKfgnJZDeQPzKTYm9vEA9g9ZrvbYUenpCXlZxWHsiF6hW7FCEgL8mAigZoJcYANhVoSH9FnME4a7FgZNfOlLEobqKtNRKXqrkWNDAXZ_ZiY5UnPPy6E9399h4fR8xpQspKY0SexpeU_aW_yv40nP8xH-ptP0E</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>AI physical simulation method and system for calculating differential operator on unstructured grid</title><source>esp@cenet</source><creator>SUN HAO ; YU FAN ; ZENG BOCHENG ; ZHANG YI ; CHENG ZERUIZHI ; LIU HONGSHENG</creator><creatorcontrib>SUN HAO ; YU FAN ; ZENG BOCHENG ; ZHANG YI ; CHENG ZERUIZHI ; LIU HONGSHENG</creatorcontrib><description>The invention relates to an AI physical simulation method and system for calculating a differential operator on an unstructured grid, and the method comprises the steps: carrying out the padding of input initial condition data, and constructing a sparse unstructured grid; calculating a differential operator item on the unstructured grid, embedding the differential operator item in the equation into a network structure, and predicting a system state quantity under the unstructured coarse grid by adopting a message passing graph neural network; and embedding the boundary condition of the equation into the simulation model by adopting a mode of simultaneously padding in the hidden space, so that the simulation model always meets the boundary condition, and AI physical simulation is carried out on the fluid. According to the method, under the condition that sparse training samples are used, the simulation precision of the complex physical field on the non-structural coarse grid is remarkably improved. 本发明涉及一种在非结构</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240903&amp;DB=EPODOC&amp;CC=CN&amp;NR=118586182A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,777,882,25545,76296</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240903&amp;DB=EPODOC&amp;CC=CN&amp;NR=118586182A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>SUN HAO</creatorcontrib><creatorcontrib>YU FAN</creatorcontrib><creatorcontrib>ZENG BOCHENG</creatorcontrib><creatorcontrib>ZHANG YI</creatorcontrib><creatorcontrib>CHENG ZERUIZHI</creatorcontrib><creatorcontrib>LIU HONGSHENG</creatorcontrib><title>AI physical simulation method and system for calculating differential operator on unstructured grid</title><description>The invention relates to an AI physical simulation method and system for calculating a differential operator on an unstructured grid, and the method comprises the steps: carrying out the padding of input initial condition data, and constructing a sparse unstructured grid; calculating a differential operator item on the unstructured grid, embedding the differential operator item in the equation into a network structure, and predicting a system state quantity under the unstructured coarse grid by adopting a message passing graph neural network; and embedding the boundary condition of the equation into the simulation model by adopting a mode of simultaneously padding in the hidden space, so that the simulation model always meets the boundary condition, and AI physical simulation is carried out on the fluid. According to the method, under the condition that sparse training samples are used, the simulation precision of the complex physical field on the non-structural coarse grid is remarkably improved. 本发明涉及一种在非结构</description><subject>CALCULATING</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyz0KAjEQQOE0FqLeYfYAFlGUbZdF0cbKfgnJZDeQPzKTYm9vEA9g9ZrvbYUenpCXlZxWHsiF6hW7FCEgL8mAigZoJcYANhVoSH9FnME4a7FgZNfOlLEobqKtNRKXqrkWNDAXZ_ZiY5UnPPy6E9399h4fR8xpQspKY0SexpeU_aW_yv40nP8xH-ptP0E</recordid><startdate>20240903</startdate><enddate>20240903</enddate><creator>SUN HAO</creator><creator>YU FAN</creator><creator>ZENG BOCHENG</creator><creator>ZHANG YI</creator><creator>CHENG ZERUIZHI</creator><creator>LIU HONGSHENG</creator><scope>EVB</scope></search><sort><creationdate>20240903</creationdate><title>AI physical simulation method and system for calculating differential operator on unstructured grid</title><author>SUN HAO ; YU FAN ; ZENG BOCHENG ; ZHANG YI ; CHENG ZERUIZHI ; LIU HONGSHENG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN118586182A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>SUN HAO</creatorcontrib><creatorcontrib>YU FAN</creatorcontrib><creatorcontrib>ZENG BOCHENG</creatorcontrib><creatorcontrib>ZHANG YI</creatorcontrib><creatorcontrib>CHENG ZERUIZHI</creatorcontrib><creatorcontrib>LIU HONGSHENG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>SUN HAO</au><au>YU FAN</au><au>ZENG BOCHENG</au><au>ZHANG YI</au><au>CHENG ZERUIZHI</au><au>LIU HONGSHENG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>AI physical simulation method and system for calculating differential operator on unstructured grid</title><date>2024-09-03</date><risdate>2024</risdate><abstract>The invention relates to an AI physical simulation method and system for calculating a differential operator on an unstructured grid, and the method comprises the steps: carrying out the padding of input initial condition data, and constructing a sparse unstructured grid; calculating a differential operator item on the unstructured grid, embedding the differential operator item in the equation into a network structure, and predicting a system state quantity under the unstructured coarse grid by adopting a message passing graph neural network; and embedding the boundary condition of the equation into the simulation model by adopting a mode of simultaneously padding in the hidden space, so that the simulation model always meets the boundary condition, and AI physical simulation is carried out on the fluid. According to the method, under the condition that sparse training samples are used, the simulation precision of the complex physical field on the non-structural coarse grid is remarkably improved. 本发明涉及一种在非结构</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language chi ; eng
recordid cdi_epo_espacenet_CN118586182A
source esp@cenet
subjects CALCULATING
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
title AI physical simulation method and system for calculating differential operator on unstructured grid
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-19T13%3A44%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=SUN%20HAO&rft.date=2024-09-03&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN118586182A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true