{"id":1626,"date":"2022-07-08T19:45:15","date_gmt":"2022-07-08T19:45:15","guid":{"rendered":"https:\/\/ieeecscai.wpengine.com\/2023\/?page_id=1626"},"modified":"2022-07-11T17:57:38","modified_gmt":"2022-07-11T17:57:38","slug":"digital-twins-dt-challenges","status":"publish","type":"page","link":"https:\/\/cai.ieee.org\/2023\/digital-twins-dt-challenges\/","title":{"rendered":"DIGITAL TWINS (DT) CHALLENGES"},"content":{"rendered":"\t\t
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INDUSTRIAL AI
DIGITAL TWINS (DT) CHALLENGES<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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CHALLENGE AREAS<\/strong><\/span><\/h3>
Effective utilization of design models for Digital Twins and PHM<\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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TECHNOLOGY CHALLENGES<\/strong><\/span><\/h3>
  • Interpretability, explainability and trust related to inferences produced by AI-based digital twins (DTs)<\/li>
  • Ability to incorporate domain and physics-based constraints into DT<\/li>
  • General robustness of model in presence of noise and other sources of variations in the input<\/li>
  • Adversarial robustness of model, and safety related to its application, e.g., adversarial perturbations in the input to fool model inference<\/li>
  • Optimal decision support arising from inference: allocation of human v\/s machine decision making, design of human-in-the-loop systems<\/li>
  • In the context of Control systems, defensible linearization of DT output to enable ease of application in PID loops<\/li>
  • Validation and verification process of Digital Twin model so outcomes can be proved to be safe and assured<\/li>
  • Lack of labels to permit fully supervised training of DTs<\/li>
  • Scalable approach for continuous learning and management of DTs over time<\/li>
  • Need for scalable Federated\/non-centralized learning in cases where data cannot be made centrally available due to various reasons<\/li>
  • Manage a network of DTs that interact and participate in a larger industrial ecosystem: scalable uncertainty propagation and management in system-of-systems network<\/li>
  • Scalable DTs that can operate on the Edge with high performance and low footprint<\/li>
  • DTs for reliable long-term forecasting (months look ahead)<\/li><\/ul><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

    INDUSTRIAL AI DIGITAL TWINS (DT) CHALLENGES CHALLENGE AREAS Effective utilization of design models for Digital Twins and PHM TECHNOLOGY CHALLENGES Interpretability, explainability and trust related to inferences produced by AI-based digital twins (DTs) Ability to incorporate domain and physics-based constraints into DT General robustness of model in presence of noise and other sources of variations […]<\/p>\n","protected":false},"author":5,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"footnotes":"","_members_access_role":[],"_members_access_error":""},"class_list":["post-1626","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages\/1626","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/comments?post=1626"}],"version-history":[{"count":0,"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/pages\/1626\/revisions"}],"wp:attachment":[{"href":"https:\/\/cai.ieee.org\/2023\/wp-json\/wp\/v2\/media?parent=1626"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}