Summary of Conditional Score-based Diffusion Models For Solving Inverse Problems in Mechanics, by Agnimitra Dasgupta and Harisankar Ramaswamy and Javier Murgoitio-esandi and Ken Foo and Runze Li and Qifa Zhou and Brendan Kennedy and Assad Oberai
Conditional score-based diffusion models for solving inverse problems in mechanics
by Agnimitra Dasgupta, Harisankar Ramaswamy, Javier Murgoitio-Esandi, Ken Foo, Runze Li, Qifa Zhou, Brendan Kennedy, Assad Oberai
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a novel framework for Bayesian inference using conditional score-based diffusion models to solve inverse problems in mechanics. The approach utilizes generative models to approximate the score function of a conditional distribution, enabling efficient sampling of the posterior distribution via Markov chain Monte Carlo schemes. The method can accommodate complex measurement noise and black-box forward models, making it suitable for high-dimensional inverse problems. The framework is demonstrated on various examples involving synthetic data and real-world elastography experiments, showcasing its ability to handle different measurement modalities, complex patterns, non-Gaussian noise, and nonlinear forward models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses a special kind of artificial intelligence called Bayesian inference to solve puzzles in mechanics. Mechanics is the study of how things move and respond to forces. In this case, scientists are trying to figure out what properties different materials have based on how they behave when subjected to stress. They’re using a new way of doing this that involves creating many possible versions of the measurement data and then combining them to get an accurate answer. This method is good at handling noisy or incomplete data and can be used for big problems with lots of variables. |
Keywords
» Artificial intelligence » Bayesian inference » Synthetic data
