Dear Scholars
How is it possible to define an algorithm for data augmentation and use a surrogate model? In particular, I want to use data farming to create artificial data instead of real data because it is cheaper than real data.
We won't have to use numerical simulations or experimental tests to gather data while using such an algorithm. To do this sort of data farming, we need a standard deviation and mean value of a parameter.
Any idea, suggestion, or paper, for data augmentation, or data farming related to my question could be a great help and valuable.
Thank you in advance for your contribution.
Look this from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF
import numpy as np
# Generate some sample data
X = np.random.rand(100, 2) # 100 samples with 2 input variables
y = np.sin(10 * X[:, 0]) + np.cos(10 * X[:, 1]) # Output variable
# Train a Gaussian Process Regression model as the surrogate
kernel = RBF(length_scale=1.0)
gpr = GaussianProcessRegressor(kernel=kernel)
gpr.fit(X, y)
# Use the surrogate model to make predictions
X_new = np.array([[0.5, 0.5]])
y_pred, y_std = gpr.predict(X_new, return_std=True)
print("Predicted output:", y_pred[0])
print("Predicted standard deviation:", y_std[0])
Ok. This code is not related to my question.
Maybe my explanation is not clear enough. Please have a look to this:
We have a process that we want to replace the process with Machine Learning.
From the process, we have 3 points with their response that we could verify the responses using them.
The goal is to create artificial data according to available knowledge, then, create a neural network, and predict the response of the process with the available knowledge.
we have information about the distribution type of the process.
So, to sum up:
1. We have very small, but good data of a parameter (e.g., 4 input data with their responses extracted via real process).
2. First we create Artificial Data using std and mu.
3. Second we create ANN to predict the response of the artificial data.
**The question is not related to image processing**
Please share any info you have about this.