This project combines classical Mars climate simulations with neural network surrogates and differentiable simulators for parameter calibration.

Neural network surrogates provide fast approximations to the classical simulations, while differentiable simulators enable gradient-based calibration of model parameters. Together, these approaches support efficient exploration of Mars climate models and their sensitivity to physical parameters.