大阪大学　数理・データ科学教育研究センター
Center for Mathematical Modeling and Data Science,Osaka University

Enabling approaches for real-time deployment, calibration, and UQ for digital twins

Tan Bui-Thanh (The University of Texas at Austin)

1. ホーム
2. セミナー・集中講義
3. セミナー
4. Enabling approaches for real-time deployment, calibration, and UQ for digital twins

大阪大学 数理・データ科学セミナー 数理モデルセミナーシリーズ 第30回

Enabling approaches for real-time deployment, calibration, and UQ for digital twins

Tan Bui-Thanh (The University of Texas at Austin)

Digital twins (DTs) are digital replicas of systems and processes. At the core of a DT is a physical/mathematical model which captures the behavior of the real system across temporal and spatial scales. One of the key roles of DTs is enabling “what if” scenario testing of hypothetical simulations to understand the implications at any point throughout the life cycle of the process, to monitor the process, and to calibrate parameters to match the actual process. In this talk we will present two real time approaches: 1) mcTangent (a model-constrained tangent slope learning) approach for learning dynamical systems; and 2) TNet (a model-constrained Tikhonov network) approach for learning inverse solutions. Both theoretical and numerical results for various problems including transport, heat, Burgers and Navier-Stokes equations will be presented.

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主催：大阪大学　数理・データ科学教育研究センター
共催：科学研究費助成事業費　学術変革領域研究(B)　計画研究
　　　「トップダウン型形状設計モデリングの数理基盤構築と工学設計問題への展開」
　　　研究代表者：矢地謙太郎（大阪大学）
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