PINNeAPPle: A Unified Platform for Physics-Informed Artificial Intelligence

This article introduces PINNeAPPle, an innovative platform aimed at bridging the gap between physical data, geometric modeling, and machine learning. Designed specifically for researchers and industrial users of Physics-Informed Neural Networks (PINNs), it provides end-to-end workflow support from data preparation to model deployment, addressing the pain point of tool fragmentation in PINN applications.

Physics-Informed Neural Networks (PINNs) embed physical laws into the training process and have great potential in fields like fluid mechanics and heat transfer. However, in practical applications, there is a problem of tool fragmentation (needing to switch between Python, CAD, deep learning frameworks, etc.), which is inefficient and error-prone, becoming a bottleneck for the widespread application of PINNs. The PINNeAPPle project was thus born, integrating three layers of "flesh": physics, geometry, and machine learning.

PINNeAPPle adopts a modular architecture, with core components including: Data Management Module (unified interface, data preprocessing), Geometric Modeling Engine (supports CAD import and mesh generation), Neural Network Builder (flexible architecture design), Physical Constraint Definer (declarative PDE definition), Training Optimization Engine (adaptive loss balancing), and Post-processing & Visualization Tools (2D/3D field display, etc.).

PINNeAPPle has been applied in multiple domains: In aerospace, it reduces the flow field simulation time of aircraft wings (from hours to minutes); in biomedicine, it simulates blood flow in blood vessels to assist personalized treatment; in the energy industry, it is used for geothermal reservoir simulation and battery thermal management; in materials science, it predicts the properties of new materials to guide experiments.

PINNeAPPle represents an important step in the tooling of scientific machine learning. By integrating the entire workflow, it lowers the threshold for PINN applications, allowing users to focus more on the physical problems themselves. It serves as a bridge connecting AI and physical sciences, helping to understand and transform the physical world more intelligently and efficiently.

Future plans for PINNeAPPle: Short-term: expand the physical model library and enhance real-time data connectivity; mid-term: introduce symbolic regression and natural language interaction; long-term: establish an open ecosystem (open-source core, plugin mechanism, sharing platform) to make physics-informed AI a standard tool in scientific research and engineering.