A Survey of Artificial Intelligence and High Performance Computing Applications to Fusion Commercialization

The report analyzes the integration of AI and HPC into the fusion energy sector to compress development timelines and overcome technical barriers. It details applications in in-silico design iteration, materials discovery, superconducting magnet optimization, and plasma control. The document also assesses the economic viability of fusion, estimating a potential Levelized Cost of Electricity (LCOE) between 4.7 and 7.9 cents per kWh, and explores a symbiotic relationship where AI accelerates fusion development while fusion provides the firm, carbon-free power required by AI data centers.

• Fusion energy offers four primary advantages for the energy transition: it is emissions-free, utilizes abundant fuel (deuterium and lithium), provides reliable firm power to complement intermittent renewables, and has a safer profile than fission by eliminating runaway reaction risks.
• The private fusion sector has seen rapid growth, with equity investments exceeding $8 billion, of which more than $7 billion was raised since 2020. The number of fusion companies grew from 10 in 2017 to over 40 by 2024.
• AI and HPC are shifting fusion development from empirical, physical prototyping to an 'in-silico' model. This allows for 'parameter sweeping' to identify viable configurations and the use of surrogate modeling to approximate complex simulations, drastically reducing the time and cost of design iterations.
• AI is being applied to five key enabling technology categories to reach commercial viability: materials selection for extreme environments, scaling high-temperature superconductor (HTS) manufacturing, improving laser accuracy and repetition for Inertial Fusion Energy (IFE), optimizing tritium breeding ratios (TBR), and developing advanced plasma diagnostics and control systems.
• Cloud-based HPC provides critical scalability, flexibility, and confidentiality for fusion startups. For example, Realta Fusion used AWS cloud computing to execute general-purpose Particle-in-Cell (PIC) simulations, which were previously limited to specialized supercomputers.
• AI-driven predictive maintenance and remote handling systems (RHS) can reduce the Levelized Cost of Electricity (LCOE) by minimizing plant downtime and maximizing electricity output.
• Based on revised ARPA-E cost studies, the projected LCOE for fusion energy ranges from 4.7 to 7.9 cents per kWh (2024 USD), making it potentially competitive with renewables, fossil fuels with carbon capture, and nuclear fission.
• A symbiotic relationship exists between AI and fusion: AI accelerates the technical development of fusion, while fusion's operational profile as a firm, carbon-free energy source is ideal for powering the massive energy demands of AI hyperscale data centers.
• Governments are using Public-Private Partnerships (PPPs) and milestone-based payment systems to de-risk investment. Examples include the UK's Fusion Industry Programme and the US's Milestone-Based Fusion Development Program (MBFDP).
• Regulatory frameworks are evolving to distinguish fusion from fission. The UK amended its Energy Act in 2023 to clarify that fusion would not be regulated under fission regulations, and the US NRC voted in 2023 to regulate fusion under the byproduct materials regulatory regime.

APA

Clean Air Task Force (2024). A Survey of Artificial Intelligence and High Performance Computing Applications to Fusion Commercialization. https://cdn.catf.us/wp-content/uploads/2024/11/04152104/fusion-ai-report.pdf

Chicago

Clean Air Task Force. A Survey of Artificial Intelligence and High Performance Computing Applications to Fusion Commercialization. 2024. https://cdn.catf.us/wp-content/uploads/2024/11/04152104/fusion-ai-report.pdf.

Wikipedia

{{cite report |author=Clean Air Task Force |title=A Survey of Artificial Intelligence and High Performance Computing Applications to Fusion Commercialization |date=November 2024 |url=https://cdn.catf.us/wp-content/uploads/2024/11/04152104/fusion-ai-report.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{cleanairtaskforce2024survey, author = {{Clean Air Task Force}}, title = {{A Survey of Artificial Intelligence and High Performance Computing Applications to Fusion Commercialization}}, institution = {Clean Air Task Force}, year = {2024}, month = nov, url = {https://cdn.catf.us/wp-content/uploads/2024/11/04152104/fusion-ai-report.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }