Hybrid Physics–ML Predictive Modeling for Complex Systems
Project HorizonSense
Client:
NDA-protected enterprise
Duration:
11 months
Team:
2 ML engineers, 1 applied mathematician, 1 software engineer
The Problem
Teams needed a dependable way to predict system behavior, explore scenarios, and understand sensitivity across changing conditions.
What We Did
Developed a physics-informed ML model, integrated differentiable simulation modules, and added a sensitivity analysis engine.
Outcome
Accurate and interpretable predictions, reduced experimentation time, and consistent performance across real-world conditions.
Operational Impact
Reliable forecasts. Scenario exploration. Sensitivity-driven insights.
Key Challenges
1
Hybrid Model Balance
Aligning physical constraints with learned ML components.
2
Parameter Space Stability
Ensuring the model behaved reliably across large ranges.
What Made This Work
Physics-ML Fusion
Combining physics-based models with machine learning for robust predictions.
High Interpretability
Transparent model behavior with clear sensitivity analysis.
Robust Scenario Analysis
Reliable performance across diverse real-world conditions.