Summary of Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data, by Jice Zeng et al.
Solving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data
by Jice Zeng, Yuanzhe Wang, Alexandre M. Tartakovsky, David Barajas-Solano
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel probabilistic inversion method based on normalizing flows is introduced for high-dimensional inverse problems. The approach combines two networks: a summary network for data compression and an inference network for parameter estimation. The summary network encodes raw observations into a fixed-size vector of summary features, while the inference network generates samples from the approximate posterior distribution of model parameters based on these features. This method is demonstrated to accurately estimate the parameter posterior distribution and predictive posterior distribution at a fraction of the time required by likelihood-based methods such as PEST-IES. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to solve really big problems is being developed using special kinds of computer models. These models are good at taking in lots of information and making predictions about things we can’t see or measure directly. The new method works by breaking down the problem into smaller pieces, compressing the data, and then using that compressed data to make educated guesses about what’s going on. This approach is really fast compared to other methods and can give us a good idea of what’s happening even when we don’t have all the information. |
Keywords
» Artificial intelligence » Inference » Likelihood
