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InverseProblems

InverseProblems is a

  • educational Inverse Problem or Numerical Method library. The goal is to provide students with a light-weighted code to explore these areas and interactive lectures with amazing Jupyter Notebook.
  • benchmark repository originally designed to test unscented Kalman inversion and other derivative-free inverse methods. The goal is to provide reseachers with access to various inverse problems, while enabling researchers to quickly and easily develop and test novel inverse methods.

Code Struction

  • All the inverse methods are in Inversion folder
  • Each other folder contains one category of inverse problems

Tutorial

Let's start! (⚠️ under construction)

Submit an issue

You are welcome to submit an issue for any questions related to InverseProblems.

Here are some research papers using InverseProblem

  1. Daniel Zhengyu Huang, Tapio Schneider, and Andrew M. Stuart. "Iterated Kalman Methodology For Inverse Problems / Unscented Kalman Inversion."

  2. Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, and Andrew M. Stuart. "Efficient Derivative-free Bayesian Inference for Large-Scale Inverse Problems."

  3. Shunxiang Cao, Daniel Zhengyu Huang. "Bayesian Calibration for Large-Scale Fluid Structure Interaction Problems Under Embedded/Immersed Boundary Framework."

  4. Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, and Andrew M. Stuart. "Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance."

  5. Daniel Zhengyu Huang, Jiaoyang Huang, and Zhengjiang Lin. "Convergence Analysis of Probability Flow ODE for Score-based Generative Models."

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