High Gain Fusion Target Design using Generative Artificial Intelligence

TL;DR

This paper introduces a novel approach using generative AI to design and stabilize fusion targets with topological configurations, enabling high energy yields at low input energy across various fusion methods.

Contribution

It presents a new AI-based method leveraging topological and canonical transformation principles for designing practical fusion targets with high energy output.

Findings

Targets can yield up to 10 GJ of energy

Driven by as little as 3 MJ of absorbed energy

Applicable to multiple fusion methods

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. The result is practical, room temperature targets that can 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, enabling 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.