Integrating deep learning and multiscale simulations, enhanced by parallel computing, to enable rapid, intelligent design of materials.
Data-driven approaches and scientific machine learning for glucose prediction and control in diabetes to enhance real-time decision-making and optimize therapeutic strategies.
Physics-Informed Machine Learning enhances battery, plastics, and biomolecule design by integrating data-driven models with physical principles to improve performance, durability, and sustainability in engineering applications.