Computing implied volatility via inverse neural networks

INST: L_200In financial mathematics volatility is computed using the Black-Scholes formula for the option price in terms of the parameters, and invert the function to get the result. Considering that this is by its nature an inverse-problem, the goal of my masters project is to explore ways how we could invert neural networks. The idea is to train a network that computes the option price for a given volatility as the input and then, get the implied volatility by inverting the already trained network