Traditional approaches to predictive modeling often rely on either physics-based models, which offer interpretability but face challenges in handling complex systems, or data-driven methods, which can learn intricate patterns but require large, high-quality data.
Our lab focuses on physics-informed learning, which integrates physical principles into machine learning models to enhance accuracy, generalization, and interpretability. By embedding domain knowledge, these approaches reduce reliance on large datasets and improve robustness under varying operational conditions.
