Preprint / Version 3

AI and Machine Learning In Nuclear Fusion




Algebraic Topology, Artificial Intelligence, Computational Analysis, Data Mining, Data Pipelines, Data Visualization, Decentralization, Distributed Computing, Entropy, Logic, Logical Proof, Logic Proofs, Machine Learning, Nuclear Fusion, Parallel Processing, Proof Theory


With the emergence of regressional mathematics and algebraic topology comes advancements in the field of artificial intelligence and machine learning. Such advancements when looking into problems such as nuclear fusion and entropy, can be utilized to analyze unsolved abnormalities in the area of fusion related research. Proof theory will be utilized throughout this paper. For logical mathematical proofs: n represents an unknown number, e represents point of entropy, and m represents maximum point, f represents fusion. This paper will look into analysis of the topic of nuclear fusion and unsolved problems as hardness problems and attempt to formulate computational proofs in relation to entropy, fusion maximum, heat transfer, and entropy transfer mechanisms. This paper will not only be centered around logical proofs but also around computational mechanisms such as distributed computing and its potential role in analyzing computational hardness in relation to fusion related problems. We will summarize a proposal for experimentation utilizing further logical proof formalities and the decentralized-internet SDK for a computational pipeline in order to solve fusion related hardness problems.


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2020-09-29 — Updated on 2020-09-29