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Cislunar Data Synthesis
Technology that forecasts space weather in advance and provides information users can understand, enabling rapid protection of social infrastructure on Earth and future lunar activities.

Overview

Challenge
As dependence on space infrastructure grows and activity areas expand, countermeasures for space weather become increasingly important.

Approach
Combines AI, computing and physics to deliver comprehensive forecasts tailored to user needs.

Value
Supports operational decisions in aviation, communications, power, positioning and lunar activities.

From Pre-Flare to SEP arrival, the technology predicts in stages using radiation occurrence probability, regression forecasting and a physics-AI integrated model

Representative figure: from Pre-Flare to SEP arrival, the technology predicts in stages using radiation occurrence probability, regression forecasting and a physics-AI integrated model.

Technical points

  • Explainable AI: Uses limited data and presents the reasons behind predictions.
  • Radiation occurrence probability prediction: Determines early whether radiation is likely to arrive.
  • Radiation regression prediction: Forecasts how the situation will evolve over time.
  • Physics-AI integrated model: Uses coupled simulation across observation, numerical calculation and AI to predict when and how much impact will occur.

Process / approach

The technology aims to provide explainable solar-radiation forecasts that show concrete impacts based on similarity to past events. When new flares or CMEs occur, it updates radiation forecasts and similar-case presentation in a form that helps users take countermeasures.

Applications

Route changes / Alternative communications / Failure response / Drone alerts / Uncrewed activity stop / Crewed activity evacuation

On Earth, it supports aviation, communications, power/railway and positioning. On the Moon, it supports the safety of uncrewed and crewed activities.