Physics-Informed Machine Learning: Engineering AI that Respects Natural Laws

Physics-Based Machine Learning (PBML), a subfield of Scientific Machine Learning (SciML), integrates data-driven models with fundamental physical principles to ensure that AI predictions remain consistent with established laws of nature. Led by Erdzan Hodzic, RISE network in this field applies these models across manufacturing, materials and production, fire and safety, and medical applications.

How It Works

By embedding differential equations and other physical constraints directly into neural networks, PBML approaches achieve robust performance even when data is sparse or noisy, a frequent challenge in engineering and scientific research. Current applications span flow and mechanical engineering optimization, indoor and fire scenario predictions as well as bio-mechanical predictions.

Dual Advantage

This hybrid paradigm offers a dual advantage: it accelerates scientific discovery while preserving physical plausibility. As the field evolves, RISE researchers are also investigating how these physics-informed frameworks can support the discovery of new materials and predict complex multi-material behaviors.

Physics for AI

By unifying centuries of scientific insight with modern data-driven methods, these approaches enable engineers to design safer, more efficient systems with fewer experiments and increased confidence in model predictions.

Looking ahead, the same principles may help shape future generations of AI architecture itself. Embedding physical laws into the very structure of neural networks will lead to more stable, data-efficient, and physically grounded models: not only AI for physics, but also Physics for AI as a foundational design principle that can guide how AI is built.