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

这项研究计划探索将物理信息神经网络(PINN)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。该项目寻求利用机器学习方法来构建可以与传统模拟工具进行测试的反应堆模型。通过使用PINN，减少了纯神经网络对海量数据的依赖，因为PINN结合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运行创造虚拟现实环境提供了机会。这个博士项目的主要目标是展示机器学习技术，特别是PINN，在反应堆建模中的有效性，并将它们的性能与传统的模拟方法进行比较。在这方面取得成功，有望通过将物理模型和实验数据整合到学习过程中来简化和加快反应堆模拟器的开发。此外，提出的方法旨在为零功率仿真器的开发定义一个统一的框架，包括当前和未来的应用。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，对提高核工程中的安全性、效率，更重要的是可获得性具有重要意义。

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.