Machine Learning Meets Heliophysics: A New Era of Discovery!

Quantum computing enthusiasts, it's time to get excited about heliophysics! This interdisciplinary field, studying the Sun and its interactions with the solar system, is undergoing a transformation with the adoption of machine learning techniques. As we've seen in many scientific disciplines, machine learning is becoming the fourth pillar of scientific discovery, and heliophysics is no exception. The heliophysics community is now facing the challenge of overcoming the barrier of technical skills posed by machine learning that are not generally mastered by the typical scientist. We need to fully appreciate and critically understand what is within reach in a few years and what could be achieved in a decade. The rapid transition from an exploratory phase to a more mature phase of research is showing a higher success rate and compelling results. Among the many possible applications of machine learning in heliophysics, a few stand out as having the potential for groundbreaking discoveries in the near future. These include reduced order modeling for accelerating computationally expensive physics-based models, physics-informed machine learning that combines the strengths of data-driven and physics-based approaches, and data-driven discovery of new physical laws and parameterizations. As quantum computing continues to advance, we can expect even more astonishing breakthroughs in heliophysics and beyond. So, let's keep our eyes on the skies and embrace the power of machine learning in our pursuit of scientific knowledge!