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  • PNAS Nexus || 无需神经网络！优化离散损失解决物理学中的逆问题
    我们将该框架描述为通过结合PDE的离散形式和现代机器学习工具来优化离散损失（ODIL），以扩展它们到不良态和逆问题。 ODIL框架通过使用基于梯度和牛顿法的离散PDE的残差来最小化代价函数。 我们展示了ODIL在解决具有缺失参数和有限数据的逆问题中的有效性。
  • Optimizing a DIscrete Loss (ODIL) to solve forward and inverse problems . . .
    We introduce the Optimizing a Discrete Loss (ODIL) framework for the numerical solution of Partial Differential Equations (PDE) using machine learning tools The framework formulates numerical methods as a minimization of discrete residuals that are solved using gradient descent and Newton's methods
  • Odil is a C++11 library for the DICOM standard - GitHub
    Odil is a DICOM library which provides a user-friendly C++11 and Python API for the different parts of the DICOM standard: Reading and writing data sets with any transfer syntax
  • Ocean Development International Law - Taylor Francis Online
    If you are unsure of whether your manuscript complies with this policy, please contact the Editor-in-Chief of ODIL in advance of your submission Peer Review Policy: All review papers in this journal have undergone editorial screening and peer review
  • odil · PyPI
    ODIL (Optimizing a DIscrete Loss) is a framework for solving inverse problems for differential equations
  • Installation - Odil
    The easiest way to install Odil is to use the pre-compiled packages, either from Anaconda, or from Debian Ubuntu For Anaconda packages, simply run conda install -c conda-forge odil
  • Software – CSElab - Harvard University
    ODIL (released: Nov 4, 2023) ODIL (Optimizing a Discrete Loss) is a Python framework for solving inverse and data assimilation problems for partial differential equations





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