High Gain Fusion Target Design using Generative Artificial Intelligence

TL;DR

The paper tackles the challenge of achieving high-gain fusion by fusing topology-based target design with a renormalization-inspired AI framework. It introduces the Ubuntu Fusion Target concept driven by magnetic-pressure implosions and four-laser drives, and a two-stage AI pipeline employing the generating functional $S_m[f(x)](z)$ and the Heisenberg Scattering Transformation (HST) to model, renormalize, and stabilize nonlinear collective dynamics. It provides reasoning and evidence that 3D implosion with 2.5D burn could achieve up to 10 GJ yield from ~3 MJ input, and that fast surrogates enable Bayesian design and rapid optimization. The work aims at practical room-temperature fusion targets with simplified fabrication and scalable AI-driven optimization, supported by theoretical links to renormalization, topology, and Lie group symmetries.

Abstract

By returning to the topological basics of fusion target design, Generative Artificial Intelligence (genAI) is used to specify how to initially configure and drive the optimally entangled topological state, and stabilize that topological state from disruption. This can be applied to all methods; including tokamaks, laser-driven schemes, and pulsed-power driven schemes. This target design philosophy has the potential to yield practical, room temperature targets that could yield up to 10 GJ of energy, driven by as little as 3 MJ of absorbed energy. The genAI is based on the concept of Ubuntu that replaces the Deep Convolutional Neural Network approximation of a functional, with the formula for the generating functional of a canonical transformation from the domain of the canonical field momentums and fields, to the domain of the canonical momentums and coordinates, that is the Reduced Order Model. This formula is a logical process of renormalization, that has the potential to enable Heisenberg's canonical approach to field theory, via calculation of the S-matrix, given observation of the fields. This can be viewed as topological characterization and control of collective, that is complex, systems.

Figures (4)

• Figure 1: Demonstration of the entanglement, that is the knotting or topology, of the magnetic field using two strings. (a) The two strings. (b) One of the strings is tied together making a loop. (c) The second string is twisted around the first string. (d) The second string is tied into a loop leading to the double helix of a Z-pinch. (e) The strings are manipulated to yield the two counter-rotating double helices of an RFP. (f) The strings are manipulated to yield a torus, as in a tokamak.
• Figure 2: X-ray self emission images of MagLIF Z-pinch stagnations: (a) with no helicity, and (b) with significant helicity.
• Figure 3: The Ubuntu laser-driven fusion target design: (a) a Be cylindrical shell, coated on the inside with solid fuel, and filled with DT gas; shown is the Z-pinch laser drive, a DALL-E rendered liner plasma tornado, an experimental back-lit image from the University of Michigan yager2018evolution of the liner plasma to the right of the plasma tornado, and a self emission image of the DD MagLIF plasma stagnation in orange, and (b) the twisted pair of the Z-pinch stagnation topology, with the return current.
• Figure 4: The Ubuntu fusion laser-drive design: (a) laser arrangement, from above, for the Z-pinch, note the wings providing return current; (b) the laser illumination pattern for the Z-pinch; (c) laser arrangement, from above, for the RFP, note the end cap providing return current; and (d) the laser illumination pattern for the RFP.