Project Overview

• Develop a PINN-based hybrid digital twin that detects precursors to process issues, monitors efficiency, and identifies root causes through physics-consistent AI.
• Merge physical models with explainable machine learning to predict deviations early and provide transparent diagnostic insights to improve efficiency, reliability, and reduce downtime.

Deliverables

• PINN Model: A unified Physics-Informed Neural Network capturing system dynamics, efficiency trends, and early degradation signals, along with training and validation data/results.
• Hybrid Digital Twin Application: A PINN-driven simulation and diagnostic engine for anomaly and root-cause detection of energy inefficiency.
• Python Codebase: End-to-end scripts for data handling, PINN training, evaluation, and deployment.
• Monitoring Interface: A lightweight dashboard for real-time anomaly alerts and interpretable diagnostic outputs.
• Technical Documentation: A complete methodological and validation report.

Technical Scope & Keywords

• Focus areas include Hybrid Digital Twin, Physics-Informed Neural Networks (PINNs), Process Simulation, Dynamic Behavior Prediction, Data-Driven Modeling, Root Cause Analysis, and Explainable AI.
• Work involves model development (PINNs), data preparation, model validation, interpretability methods, dashboarding for monitoring and alerts, and preparing reproducible code and documentation.

Application

• To apply, send your CV to careers@integrationobjects.com .
• In your application, emphasize experience with PINNs or physics-guided ML, process simulation, model validation, and building deployable Python codebases.