Application of the machine learning methods for development of zero-power reactor simulator

该研究计划探索将物理信息神经网络（PINNs）集成到反应堆模拟中，旨在彻底改变传统的建模和模拟方法。该项目旨在利用机器学习方法来构建反应堆模型，这些模型可以通过传统的模拟工具进行测试。通过使用PINN，减轻了纯神经网络对大量数据的依赖，因为PINN包含了完善的物理方程来增强预测能力。这种创新的方法不仅减轻了数据收集的负担，而且为反应堆培训和操作创造了虚拟现实环境。这个博士项目的主要目标是证明机器学习技术，特别是PINN在反应堆建模中的有效性，将其性能与传统的模拟方法进行比较。在这方面的成功，承诺简化和加快反应堆模拟器的开发，将物理模型和实验数据集成到学习过程中。此外，所提出的方法的目的是定义一个统一的框架，零功率模拟器的发展，包含当前和未来的应用。通过这项研究，探索了机器学习在推进反应堆模拟技术方面的潜力，对提高核工程的安全性、效率以及更重要的可访问性具有重要意义。

This research program explores the integration of Physics Informed Neural Networks (PINNs) into reactor simulations, aiming to revolutionize traditional modeling and simulation approaches. The project seeks to leverage machine learning methods to construct reactor models that can be tested against conventional simulation tools. By employing PINNs, the reliance on vast amounts of data for pure Neural Networks is mitigated, as PINNs incorporate well-established physical equations to enhance predictive capabilities. This innovative approach not only reduces the burden of data collection but also presents opportunities for creating virtual reality environments for reactor training and operation. The primary objective of this PhD project is to demonstrate the efficacy of machine learning techniques, specifically PINNs, in reactor modeling, comparing their performance against traditional simulation methods. Success in this, promises to simplify and expedite the development of reactor simulators by integrating physical models and experimental data into the learning process. Moreover, the proposed approach aims to define a unified framework for the development of zero-power simulators, containing both current and future applications. Through this research, the potential of machine learning in advancing reactor simulation technology is explored, with implications for enhancing safety, efficiency, and more importantly accessibility in nuclear engineering.