 We introduce a new class of volatility operators which map the square integrable noise into the Filipovi \'{c space of forward curves , and we specify a deterministic parametrized version of it. For calibration purposes, we train a neural network to approximate the  option price as a function of the model parameters.
<coherence>  We introduce a new class of volatility operators which map the square integrable noise into the Filipovi \'{c space of forward curves , and we specify a deterministic parametrized version of it. For calibration purposes, we train a neural network to approximate the  option price as a function of the model parameters.
 We introduce a new class of volatility operators which map the square integrable noise into the Filipovi \'{c space of forward curves , and we specify a deterministic parametrized version of our model and train a neural network to approximate the option price as a function of the model parameters.