Demystifying AI in Nuclear Engineering: The Role of pyMAISE

Demystifying AI in Nuclear Engineering: The Role of pyMAISE

As global efforts to reduce carbon emissions intensify, nuclear energy emerges as a key player, offering vast amounts of clean, reliable power. However, the nuclear industry is subject to stringent safety regulations, particularly in the United States, where the Nuclear Regulatory Commission (NRC) enforces rigorous standards. As such, integrating advanced technologies like artificial intelligence (AI) into this sector demands not only innovation but also transparency and explainability.

Artificial intelligence and machine learning hold immense potential to accelerate the design process for new reactors and enhance the safety measures of existing ones. Nevertheless, these technologies are often likened to a “black box” because their decision-making processes are not easily interpretable. This opacity presents a challenge in a heavily regulated domain where every operational aspect must be thoroughly understood and vetted.

To address these challenges, researchers at the University of Michigan have introduced pyMAISE (Python-based Michigan Artificial Intelligence Standard Environment). This innovative tool is designed to enhance model transparency and improve compliance with regulatory standards in nuclear engineering.

pyMAISE simplifies the development and testing of AI models, enabling nuclear engineers, even those without extensive AI expertise, to create sophisticated and reliable models. It facilitates automatic benchmarking across a range of models, from basic linear regressions to complex neural networks, thereby identifying the most efficient models while optimizing computational resources through parallel processing.

The efficacy of pyMAISE has been demonstrated in three critical applications focused on reactor design and safety monitoring. Its performance surpassed other well-regarded benchmarking libraries such as Auto-Sklearn, AutoKeras, and H2O. Notably, it proved effective even when dealing with limited datasets, a common challenge in the field.

One of the standout features of pyMAISE is its preliminary explainability capabilities, which highlight inputs that most significantly impact a model’s output. This is crucial for the nuclear sector, where licensing and approval processes rely heavily on model transparency and interpretability. As explainability improves, the potential for broader application of AI in other safety-critical fields, such as healthcare and finance, becomes more tangible.

In summary, pyMAISE marks a significant advancement in integrating AI into nuclear energy applications. By balancing rapid technological progress with stringent regulatory requirements, it ensures safer and more efficient processes. Moreover, by fostering transparent and understandable AI models, pyMAISE could accelerate the acceptance of AI across various regulatory landscapes, contributing to a safer, more sustainable future.