PROTON: A Radiation Monitoring Framework Combining Physics-Informed Neural Networks and Edge Computing

PROTON is a distributed scientific machine learning framework that integrates Physics-Informed Neural Networks (PINN), stochastic quantum diffusion modeling, and spatio-temporal Fourier Neural Operators (FNO) to achieve real-time radiation monitoring and spatial mapping on ESP32 edge sensor networks. The project aims to address the issues of isolation and data latency in traditional radiation detection devices, providing an efficient radiation monitoring solution through modular design and collaboration between edge computing and central processing.

Radiation monitoring is crucial in nuclear safety, environmental monitoring, and industrial applications. Traditional devices are mostly isolated single-point measurement tools, with significant time delays in data collection and analysis. The PROTON project aims to build a complete pipeline from desktop sensors to distributed spatial mapping, using a laptop as the computing center to enable real-time monitoring and intelligent analysis. The choice of FNIRSI GC-01 radiation detector with Rad Pro firmware flashed reflects the emphasis on integrating open-source ecosystems and scalability.

PROTON adopts a modular design, including: 1. Hardware interface layer: Encapsulates the Rad Pro communication protocol (request-response mode, requiring active polling) via radpro_serial.py; 2. Physics-Informed Neural Network (PINN): Embeds physical constraints such as radiation propagation attenuation laws to improve model generalization and physical consistency; 3. Stochastic quantum diffusion modeling: Simulates the random distribution of pulse arrival times, supporting anomaly detection and dose estimation based on Poisson statistical properties; 4. Spatio-temporal Fourier Neural Operator (FNO): Extends to spatio-temporal dimensions, efficiently models partial differential equation solution operators, and provides a foundation for real-time spatial mapping.

PROTON follows a layered design: Data acquisition layer (ESP32 edge nodes are responsible for local detection and preprocessing), communication layer (USB serial port enables data exchange with the central unit), computing layer (laptop runs complex algorithms such as PINN, diffusion models, and FNO), and application layer (real-time monitoring, spatial mapping, anomaly alerts). This architecture allocates resources rationally, using edge nodes for high-frequency data collection and the central unit for complex task processing.

The project faces three major challenges: 1. Timestamp precision limitation: Rad Pro has no individual pulse timestamps; time information is recovered through high-frequency polling cumulative counting + software interpolation/statistical modeling; 2. Physical consistency and data-driven fusion: PINN encodes radiation physics laws as soft constraints to ensure model predictions align with physical intuition; 3. Distributed synchronization issues: ESP32 mesh time synchronization and data consistency challenges are addressed by using FNO spatio-temporal modeling to process irregularly sampled data and generate continuous radiation field estimates.

PROTON's application scenarios include: Nuclear facility monitoring (real-time level monitoring and spatial dose distribution maps), environmental radiation surveys (rapidly drawing regional background maps), emergency response (real-time spatial radiation information for nuclear accidents), and scientific research (providing high-precision spatio-temporal datasets).

PROTON is a cutting-edge exploration of the integration of scientific machine learning and edge computing, combining technologies such as PINN, generative modeling, and FNO to deploy complex physical modeling on resource-constrained edge device networks. The modular design lowers the entry barrier, and future plans include adding multi-sensor fusion, real-time 3D radiation field visualization, and other features, which are of learning value to developers in related fields.