Summary of Feasibility Study on Active Learning Of Smart Surrogates For Scientific Simulations, by Pradeep Bajracharya et al.
Feasibility Study on Active Learning of Smart Surrogates for Scientific Simulations
by Pradeep Bajracharya, Javier Quetzalcóatl Toledo-Marín, Geoffrey Fox, Shantenu Jha, Linwei Wang
First submitted to arxiv on: 10 Jul 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 approach is proposed in this paper to accelerate high-performance scientific simulations, particularly those exploring extensive parameter spaces. The idea is to develop deep neural networks (DNNs) as surrogate models that can reduce computational costs. Existing methods rely on expensive simulation data, which is a challenge not thoroughly explored in the literature. This study investigates incorporating active learning into DNN surrogate training, allowing for intelligent and objective selection of training simulations. This reduces the need for extensive simulation data generation and dependency on pre-defined training simulations. The efficacy of diversity- and uncertainty-based strategies for selecting training simulations is examined in the context of constructing DNN surrogates for diffusion equations with sources, considering two different DNN architectures. The results pave the way for developing high-performance computing infrastructure for Smart Surrogates that supports on-the-fly generation of simulation data steered by active learning strategies, potentially improving efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special computers to help scientists do complex calculations faster and more efficiently. Scientists often have to run lots of simulations to understand big systems, but this can take a lot of time and computer power. One way to make it faster is to use artificial intelligence (AI) models called deep neural networks (DNNs). These DNNs can act like shortcuts for the complex calculations. The problem is that training these DNNs requires a lot of data from simulations, which can be hard to get. This paper looks at how to make this process more efficient by using an approach called active learning. This means the computer can choose which simulations are most important to run, rather than running all of them. The results show that this approach can help scientists do their calculations faster and more efficiently. |
Keywords
» Artificial intelligence » Active learning » Diffusion
