  Understanding the first billion years of the universe requires studying two
critical epochs: the Epoch of Reionization (EoR) and Cosmic Dawn (CD). However,
due to limited data, the properties of the Intergalactic Medium (IGM) during
these periods remain poorly understood, leading to a vast parameter space for
the global 21cm signal. Training an Artificial Neural Network (ANN) with a
narrowly defined parameter space can result in biased inferences. To mitigate
this, the training dataset must be uniformly drawn from the entire parameter
space to cover all possible signal realizations. However, drawing all possible
realizations is computationally challenging, necessitating the sampling of a
representative subset of this space. This study aims to identify optimal
sampling techniques for the extensive dimensionality and volume of the 21cm
signal parameter space. The optimally sampled training set will be used to
train the ANN to infer from the global signal experiment. We investigate three
sampling techniques: random, Latin Hypercube (stratified), and Hammersley
Sequence (quasi-Monte Carlo) sampling, and compare their outcomes. Our findings
reveal that sufficient samples must be drawn for robust and accurate ANN model
training, regardless of the sampling technique employed. The required sample
size depends primarily on two factors: the complexity of the data and the
number of free parameters. More free parameters necessitate drawing more
realizations. Among the sampling techniques utilized, we find that ANN models
trained with Hammersley Sequence sampling demonstrate greater robustness
compared to those trained with Latin Hypercube and Random sampling.
