Covers the computation of cost function for multivariable control systems using the LQR framework and applying gradient descent for controller improvement.
Explores explicit stabilised Runge-Kutta methods and their application to Bayesian inverse problems, covering optimization, sampling, and numerical experiments.
Explores linear quadratic regulation for optimal control of linear systems, focusing on minimizing a quadratic cost function to move the system state towards zero.
Explores response theory, phase transitions, and fluctuations in weakly interacting systems, including stochastic particles and opinion formation models.
