Search Results (3)
Smart denoising for recurrent neural network optimization
This thesis introduces a new optimization method based on deep learning, designed for data influenced by random processes. The main contribution of this method is the combination of advanced noise reduction techniques with recurrent neural network models, which helps to prevent the common problem of overfitting seen when there is …
Probabilistic grammar-based equation discovery
In this thesis, we introduce novel methods for equation discovery (ED), based on the use of probabilistic grammars. ED and symbolic regression address the task of finding a symbolic mathematical model that best describes observed data. Models can be as simple as an algebraic equation or as complex as a …
Meshless adaptive solution procedure for efficient solving of partial differential equations
Meshless methods are becoming increasingly popular in computational mechanics and engineering. Their main feature is the ability to manage complex geometries while avoiding the often tedious process of mesh generation required by the traditional methods. Various meshless approximations of linear differential operators appearing in the governing problem have been proposed …