Manuscript Topics

In recent years, machine learning algorithms have become immensely popular with successful results in a large variety of different fields. In 2021 we saw great breakthroughs in protein folding, drug discovery and weather prediction aided by ML. However, there is more work to do in terms of assessing the uncertainty and improving the performance of machine learning algorithms in general. This special issue wants to tackle this problem by welcoming both theoretical and application papers related (but not limited) to:

• Theory/application of machine learning algorithms with data assimilation/error quantification    
• Error quantification of Machine learning applications    
• Machine learning algorithms for forecasting dynamical systems with uncertainties    
• Machine learning with and for reduced-order-modeling    
• Bayesian-type neural networks    
• Data augmentation techniques in Machine learning (e.g., generative adversarial neural networks, variational auto-encoders, etc.)    
• Physics-informed machine learning    
• Review papers about machine learning with uncertainties

Keywords: Data Science; Machine Learning; Uncertainty Quantification; Physics Informed Machine Learning; Reduce Order Models

For Instructions for authors, please visit
https://www.aimspress.com/era/news/solo-detail/instructionsforauthors
Please submit your manuscript to online submission system
https://aimspress.jams.pub/