Embracing the Incomprehensible Future, Fusion Edition

Very, very complicated algorithms are starting to solve problems in ways we don’t fully understand. And it again raises the question of whether we as a species are headed into that incomprehensible future.

I think if it solves fusion, I’ll take it:

The number of design choices for optimizing this fusion plasma is enormous, because all aspects of the capsule’s dimensions and structure, as well as the details of the laser and the time dependence of the laser’s power, can be varied. Implosion performance can also be considerably affected by ‘hydrodynamic’ instabilities that are seeded by inevitable imperfections in the manufactured capsule and imbalances or instabilities in the applied laser light. Unsurprisingly, the complexity of this implosion system leads to fusion performance that is extremely sensitive to design details and instabilities.

With so many design choices, and with limited experimental data, the standard approach to optimizing fusion performance has been to use theoretical insights along with sophisticated radiation–hydrodynamic simulations that follow, as well as we know how, the physics of the implosions and their degradations.

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The authors trained a statistical model to match an initial set of experimental data using simulation outputs. They then used this model to suggest changes to the implosion design that the model predicted would improve the fusion performance.

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By consistently following this methodology to design a series of experimental campaigns, Gopalaswamy and colleagues improved the fusion yield by a remarkable factor of three compared with OMEGA’s previous record.

Experimentally trained statistical models boost nuclear-fusion performance

And the kicker:

[I]t is humbling for scientists dedicated to understanding such complex systems to recognize how much they don’t understand. As a quote attributed to physicist Eugene Wigner states: “It is nice to know that the computer understands the problem. But I would like to understand it, too”.

Our wetware brains weren’t evolved to track all these variables. But we are building machines that can.