Date of Award:


Document Type:


Degree Name:

Master of Science (MS)


Computer Science

Committee Chair(s)

Soukaina Filali Bourbrahimi


Soukaina Filali Bourbrahimi


Mario Harper


Mike Taylor


Neural networks are adept at finding patterns that are too long and too small for humans to find in data. Usually, this power is used to generate predictions with greater accuracy than most alternative models. However, we can also use this power to understand more about the data we train these networks on. We do this by changing the data that the networks train on and the data they are tested on. This allows us to both control the maximum length of a pattern and to compare data between different groups, in our case, different solar cycles. This thesis is our attempt to understand solar wind data better. We do this by proposing a physics based framework and comparing the results of different inputs and outputs through different networks. These results show three major things: 1, that training networks using the physical law of Ohm's law for an ideal plasma can improve network performance predictions; 2, that the specific characteristics of different solar cycles make them more suitable for training or testing; and 3, that while physics guided loss functions can be helpful in certain situations, they are no silver bullet to improved predictions.