Well as long as there have been electronic computers they have been applied they have been applied to the problems of magnetic confinement. For tokamaks static configurations and dynamic controls have been simulated. For stellarators geometry optimizations have been done. The time has always been right for this application.
Stellarators are particularly fun. First you optimize a last closed magnetic flux surface (a 3D sheet) with relatively simple plasma models, then you optimize coils (you need to weight things like magnetic field error, gaps big enough for heating and diagnostics, and coil complexity a la minimum radius), then you perform more accurate simulations to start the overall optimization loop again.
There are 4 episodes on fusion. You're probably referencing 312 which talks about simulating stellarator plasmas a bit.
Episode 22 is an intro to fusion power research and tokamaks, but could be skipped if you are aware of the big concepts.
Episode 157 is an interview of a director at ITER: the largest fusion power project in terms of scale, ambition, and funding. ITER is expected to demonstrate net energy gain fusion within the next ten years. Episode 157 gives a good sense of where the cutting edge of fusion power research exists currently and its potential place in society.
Episode 304 interviews the authors of a recent popular science book on tokamaks. This episode covers the history of fusion power research, and the economics involved in future power plants.
Episode 312 is a set of interviews at W7-X: the current largest stellarator. This episode take a deeper dive into what fusion research is like and how threads are being tugged on.
This same podcast also has interesting episodes on plasma physics and superconductors. The superconductor episode is especially worth listening to more than once.
Stellarators are particularly fun. First you optimize a last closed magnetic flux surface (a 3D sheet) with relatively simple plasma models, then you optimize coils (you need to weight things like magnetic field error, gaps big enough for heating and diagnostics, and coil complexity a la minimum radius), then you perform more accurate simulations to start the overall optimization loop again.