Learning Reduced Order Mappings of Navier-Stokes: An Investigation of Generalization on the Viscosity Parameter

Solving Partial Differential Equations (PDEs) in engineering such as Navier-Stokes is incredibly computationally expensive and complex. Without analytical solutions, numerical solutions can take ages to simulate at great expense. In order to reduce this cost, neural networks may be used to compute approximations of the solution for use during...

Empirical study of GANITE’s robustness to hidden confounders

An empirical study is performed exploring the sensitivity to hidden confounders of GANITE, a method for Individualized Treatment Effect (ITE) estimation. Most real world datasets do not measure all confounders and thus it is important to know how crucial this is in order to obtain comparable predictions. This is explored through the removal of...

Signal-processing of audio for speech-recognition

The transcription of voice using neural networks is a technique that deserves attention, as speech assistants are becoming increasingly popular. Neural networks have often difficulty with determining the differences between a talking person and noise. Humans have a much better understanding of this and could possibly apply their knowledge of the...

Statistical analysis to depict the influence of news headlines on stock volatility

A statistical approach to depict stock volatility based on general news headlines by exploiting Machine Learning methods such as sentiment analysis and neural networks. The results from the analysis are then used in the financial market with a particular trading strategy, called a bottom straddle. We conclude that not enough information can be...