Double Almost Done Seminar

Speaker: Chris Beeler (MCSC)
Title: Perpetually Playing Physics
Abstract: Classic control problems such as Cart-Pole and Mountain-Car are based on simple Newtonian physics. Both have been solved previously with reinforcement learning algorithms. Here, we show that reinforcement learning can also be used to solve classical problems in thermodynamics. Using a reinforcement learning method based on genetic algorithms, that we test on standard bench-marking environments, our software agent can learn to reproduce thermodynamic cycles without prior knowledge of physical laws. We have created a simulated learning environment which models a simple piston, where an agent can activate thermodynamic processes. With this method, we were able to optimize an artificial neural network based policy to maximize the thermal efficiency for several different cases. Depending on the actions available to the agent, different known cycles emerged, including the Carnot, Stirling, and Otto cycles. Importantly, we show an example of how reinforcement learning can be used to aid scientists in finding solutions to problems that have yet to be fully explored. In one of the heat engine environments, we introduced a non-adiabatic process which caused the engine to lose energy. In this case, the agent produced, what is to the best our knowledge, the best solution for the problem.

Speaker: Brady Metherall (MCSC)
Title: A Nonlinear Model for Dispersion-Tuned Actively Mode-Locked Lasers
Abstract: A new nonlinear model is proposed for tuneable lasers. Using the generalized nonlinear Schrödinger equation as a starting point, expressions for the transformations undergone by the pulse are derived for each component in the cavity. These transformations are then composed to give the overall effect of one trip around the cavity. The linear version of this model is solved analytically, and the nonlinear version numerically. A consequence of this model being nonlinear is that it is able to exhibit wave breaking which prior models could not. We highlight the rich structure of the boundary of stability for a particular plane of the parameter space.