Summary of Two-stage Surrogate Modeling For Data-driven Design Optimization with Application to Composite Microstructure Generation, by Farhad Pourkamali-anaraki et al.
Two-Stage Surrogate Modeling for Data-Driven Design Optimization with Application to Composite Microstructure Generation
by Farhad Pourkamali-Anaraki, Jamal F. Husseini, Evan J. Pineda, Brett A. Bednarcyk, Scott E. Stapleton
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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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 novel two-stage machine learning-based surrogate modeling framework addresses inverse problems in scientific and engineering fields by first identifying a limited set of candidates whose predicted outputs align with desired outcomes using the “learner” model. The “evaluator” model then assesses this reduced candidate space, eliminating inaccurate solutions guided by a user-defined coverage level. This framework integrates conformal inference for efficient and versatile applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to solve problems in science and engineering. It uses machine learning to find the best answers quickly and reliably. The method works by first finding a few good options, then checking those options to make sure they’re accurate. This makes it better than other methods that just try one thing at a time. |
Keywords
* Artificial intelligence * Inference * Machine learning
