Build NeurEco Discrete Dynamic model with GUI
Contents
Build NeurEco Discrete Dynamic model with GUI#
Fill in the Settings tab, the build parameters are explained in the table below:
Press Build button
Once the Build started, the Training, Evaluation, Metrics and Export panels become available. The moment the first model is saved to the checkpoint, these panels can be used as usual.
Build parameters#
Name 
Description 

Training Data 
Required. Data used to train a model. Click on Add files and choose paths to the files prepared according to Data preparation for NeurEco Discrete Dynamic with GUI 
Validation Data 
Optional. Data used to validate a model. If not provided, the Validation Data are chosen automatically among the provided samples in Training Data 
Testing Data 
Optional. Data never used during the training process. If provided, allow to monitor the model performance on the test data during the Build. 
Input normalization: Shift type 
Default = mean. Possible values: none, mean. See Data normalization for Discrete Dynamic for more details. 
Input normalization: Scale type 
Default = l2. Possible values: none, l2. See Data normalization for Discrete Dynamic for more details. 
Input normalization: normalize per feature 
Default = True. If True, normalizes each input feature independently from others. See Data normalization for Discrete Dynamic for more details. 
Output normalization: Shift type 
Default = mean. Possible values: none, mean. See Data normalization for Discrete Dynamic for more details. 
Output normalization: Scale type 
Default = l2. Possible values: none, l2. See Data normalization for Discrete Dynamic for more details. 
Output normalization: normalize per feature 
Default = True. If True, normalizes each output feature independently from others. See Data normalization for Discrete Dynamic for more details. 
Validation data percentage 
Default = 30.0. Percentage of the data that NeurEco selects to use as Validation Data. Ignored when Validation Data are provided explicitly. 
Advanced parameters#
Name 
Description 

steady input 
forces the built model to be stable when fed with this input value 
steady output 
stable output value associated to input value of steady input 
min hidden states 
Default = 1, defines starting number of hidden states, it can accelerate the best topology identification process 
max hidden states 
Default = 0, defines the maximum number of hidden states, it can accelerate the best topology identification process. Is not taken into account when set to 0. 
Data normalization for Discrete Dynamic#
Set normalize per feature to True if trying to fit the features of different natures (temperature and pressure for example) and want to give them equivalent importance.
Set normalize per feature to False if trying to fit the features of the same nature (a set of temperatures for example) or a field.
If neither of provided normalization options suits the problem, normalize the data your own way prior to feeding them to NeurEco (and deactivate normalization by setting the scale and shift to none).
A normalization operation for NeurEco is a combination of a \(shift\) and a \(scale\), so that:
Allowed shift methods for NeurEco and their corresponding shifted values are listed in the table below:
Name 
shift value 

none 
\[0\]

mean 
\[mean(x)\]

Allowed scale methods for NeurEco Tabular and their corresponding scaled values are listed in the table below:
Name 
scale value 

none 
\[1\]

l2 
\[\frac{\left\Vert x\right\Vert }{\sqrt{size \_ of \_ x}}\]

Control the size of the NeurEco Discrete Dynamic model during Build#
At any given moment of time the state of the system is represented by a vector, that stores the socalled hidden states of the system. In the statespace representation the hidden states are the state variables.
NeurEco Discrete Dynamic allows imposing the limits on the number of hidden states. When these limits rely on some additional knowledge about the system, it can facilitate the model training and reduce the time of Build.
Imposing the maximum number of hidden states, by setting the parameter max hidden states in Advanced settings, can decrease the size of the constructed model. That is what one is looking for when seeking a tradeoff between accuracy and augmenting the embeddability of the model even more.
See Advanced build tutorial for an example of usage.