Anna Suzuki 研究室

AI 要約（直近 5 年の研究成果）

※ AI（Claude）が、公開されている論文要旨から研究の問い・手法・主要な発見を事実情報として抽出・再構成して自動生成しています。誤りを含む可能性があるため、正確性は研究室公式情報でご確認ください。

研究成果（22 件）

• [2025] Reliable and Practical Inverse Modeling of Natural‐State Geothermal Systems Using Physics‐Informed Neural Networks: Three‐Dimensional Model Construction and Assimilation With Magnetotelluric Data
  DOI: https://doi.org/10.1029/2025jh000683
• [2025] Three-dimensional finite-volume modelling and laboratory validation of non-Darcy flow in rough rock fractures
  DOI: https://doi.org/10.1016/j.rineng.2025.106261
• [2025] Relationship between subjective wellbeing, personality traits, and lifestyle migration: A web-based survey analysis in Japan
  DOI: https://doi.org/10.1016/j.wss.2025.100312
• [2024] Investigating rough single-fracture permeabilities with persistent homology
  DOI: https://doi.org/10.5194/se-15-353-2024
• [2024] Automated parameter estimation for geothermal reservoir modeling using machine learning
  DOI: https://doi.org/10.1016/j.renene.2024.120243
• [2024] Benchmark datasets of representative geothermal reservoir models with pseudo-geophysical exploration and well data
  DOI: https://doi.org/10.1016/j.dib.2024.110828
• [2024] Modeling unobserved geothermal structures using a physics-informed neural network with transfer learning of prior knowledge
  DOI: https://doi.org/10.1186/s40517-024-00312-7
• [2023] Using 3D-printed fracture networks to obtain porosity, permeability, and tracer response datasets
  DOI: https://doi.org/10.1016/j.dib.2023.109010
• [2023] Automated Parameter Estimation for Geothermal Reservoir Modeling Using Machine Learning
  DOI: https://doi.org/10.2139/ssrn.4359084
• [2023] Analysis of large-scale performance with supercritical-water fracturing in geothermal resource development
  DOI: https://doi.org/10.1016/j.geoen.2023.211575

科研費（0 件）

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所属学会・役職（0 件）

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