Summary of Navigating Uncertainties in Machine Learning For Structural Dynamics: a Comprehensive Review Of Probabilistic and Non-probabilistic Approaches in Forward and Inverse Problems, by Wang-ji Yan (1 and 2) et al.
Navigating Uncertainties in Machine Learning for Structural Dynamics: A Comprehensive Review of Probabilistic and Non-Probabilistic Approaches in Forward and Inverse Problems
by Wang-Ji Yan, Lin-Feng Mei, Jiang Mo, Costas Papadimitriou, Ka-Veng Yuen, Michael Beer
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Dynamical Systems (math.DS)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 machine learning paper presents a comprehensive review on navigating uncertainties in machine learning (ML) models, highlighting the need for effective uncertainty awareness to enhance prediction robustness. The review categorizes uncertainty-aware approaches into probabilistic and non-probabilistic methods, emphasizing Bayesian neural networks for their superior performance and potential. Techniques and methodologies are discussed, including interval learning, fuzzy learning, and Bayesian neural networks, which are applied in structural dynamic problems like response prediction, sensitivity assessment, and reliability analysis. The paper also identifies research gaps and suggests future directions for investigations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Uncertainties in machine learning (ML) models can compromise the reliability of predictions. This paper reviews ways to navigate these uncertainties, including probabilistic methods like Bayesian neural networks and non-probabilistic methods like interval learning and fuzzy learning. The review explains how to use ML to predict structural responses, assess sensitivities, and analyze reliability. It also suggests future directions for research. |
Keywords
» Artificial intelligence » Machine learning
