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Event Information

13th Prediction Science Seminar
(Prediction Science Laboratory and RIKEN iTHEMS Joint Seminar)

Date
January 23rd, 2023 (Mon.) 10:30-12:00 (JST)
Language
English
Place
Zoom

To join the seminar, please contact the Prediction Science Seminar Office: prediction-seminar[remove here]@ml.riken.jp

Program

Time Content Speaker
10:30-11:30 Physics-informed deep learning approach for modeling crustal deformation Dr. Naonori Ueda (Deputy Director, RIKEN Center for Advanced Intelligence Project (AIP))
11:30-12:00 Discussion -

Abstract

The movement and deformation of the Earth's crust and upper mantle provide critical insights into the evolution of earthquake processes and future earthquake potentials. Crustal deformation can be modeled by dislocation models that represent earthquake faults in the crust as defects in a continuum medium. In this study, we propose a physics-informed deep learning approach to model crustal deformation due to earthquakes. Neural networks can represent continuous displacement fields in arbitrary geometrical structures and mechanical properties of rocks by incorporating governing equations and boundary conditions into a loss function. The polar coordinate system is introduced to accurately model the displacement discontinuity on a fault as a boundary condition. We illustrate the validity and usefulness of this approach through example problems with strike-slip faults. This approach has a potential advantage over conventional approaches in that it could be straightforwardly extended to high dimensional, anelastic, nonlinear, and inverse problems.

Organizer

  • Prediction Science Laboratory (RIKEN CPR)

Co-organizer

  • Data Assimilation Research Team (R-CCS)
  • RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program
  • Environmental Metabolic Analysis Research Team (RIKEN CSRS)
  • Computational Climate Science Research Team (R-CCS)
  • Medical Data Deep Learning Team (R-IH)
  • Medical Data Mathematical Reasoning Team (R-IH)
  • Laboratory for Physical Biology (RIKEN BDR)

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