Summary of Data-augmented Predictive Deep Neural Network: Enhancing the Extrapolation Capabilities Of Non-intrusive Surrogate Models, by Shuwen Sun et al.
Data-Augmented Predictive Deep Neural Network: Enhancing the extrapolation capabilities of non-intrusive surrogate models
by Shuwen Sun, Lihong Feng, Peter Benner
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA)
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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 paper addresses the challenge of numerically solving large parametric nonlinear dynamical systems using machine-learning-aided surrogates. The goal is to develop methods that can accurately generalize over a larger time interval [0, T], given training data available only up to a shorter time interval [0, T0] with T0 < T. To achieve this, the authors focus on improving the accuracy of these surrogate models by developing novel architectures and learning algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem in mathematics. It’s hard to figure out how things will change over time when there are many variables involved and it takes a lot of computer power to do so. To make this easier, scientists have been using machine-learning tools to help simplify the calculations. But even with these tools, it’s still tricky to get accurate results if you only have data up until a certain point in time. |
Keywords
* Artificial intelligence * Machine learning
