Revolutionizing Industrial Control: Hard-Constrained PINNs for Real-Time Optimization
Published:Jan 18, 2026 22:16
•1 min read
•r/learnmachinelearning
Analysis
This research explores the exciting potential of Physics-Informed Neural Networks (PINNs) with hard physical constraints for optimizing complex industrial processes! The goal is to achieve sub-millisecond inference latencies using cutting-edge FPGA-SoC technology, promising breakthroughs in real-time control and safety guarantees.
Key Takeaways
- •The project aims to implement hard constraints in PINNs for industrial process optimization.
- •FPGA-SoC deployment is planned for sub-millisecond inference.
- •Focus is on improving data efficiency and stability compared to traditional ML methods.
Reference
“I’m planning to deploy a novel hydrogen production system in 2026 and instrument it extensively to test whether hard-constrained PINNs can optimize complex, nonlinear industrial processes in closed-loop control.”