Hi Tom,
I have a completely off-topic question. This is not an issue but rather a philosophical discussion.
Let's say we have an observable output (O) and a number of features (F) that we believe have an effect of the observed output.
Let's say our data is a time series and we wish to carry out forecasting, this is, predicting future values of O on the basis of the past correlation patterns between O and F.
We suspect that some of the features are actually stimuli that trigger an unobserved swarm behaviour which, at least in part, is responsible for the outcome.
The swarm behaviour is not observable. So we do not know how the individual actors react to to the stimuli F.
In such context, would it be possible and would it make sense in your opinion to use a PINN-like strategy and somehow embed a swarm algorithm in our neural network in order to optimise its forecasting performance?
Knowing that:
Not all features F are stimuli, some may just be information.
The swarm behaviour is not absolutely deterministic and does not fully account for the observable O.
Our hypothesis, however, is that the underlying swarm behaviour represents a substantial contribution to O.
What do you think?
Best regards,
Ed Moman
Hi Tom,
I have a completely off-topic question. This is not an issue but rather a philosophical discussion.
In such context, would it be possible and would it make sense in your opinion to use a PINN-like strategy and somehow embed a swarm algorithm in our neural network in order to optimise its forecasting performance?
Knowing that:
What do you think?
Best regards,
Ed Moman