Intelligent Computational Methods for Advanced Materials and Manufacturing

We develop intelligent manufacturing systems that integrate physics-based modeling, artificial intelligence, sensing, and adaptive control. Our research focuses on advanced and additive manufacturing processes where material behavior, process conditions, and product quality are tightly connected. By combining digital twins, multimodal data, machine learning, and uncertainty-aware decision-making, we aim to improve process reliability, reduce defects, and accelerate qualification. We also investigate real-time monitoring and closed-loop control strategies that allow manufacturing systems to respond to changing conditions. The long-term goal is to enable autonomous, data-rich manufacturing platforms that can learn, adapt, and make reliable decisions at scale and with confidence.

We develop next-generation computational tools for materials and manufacturing by combining high-performance computing, GPU acceleration, artificial intelligence, and emerging hybrid computing concepts. Our work includes physics-informed machine learning, reduced-order modeling, surrogate models, adaptive sampling, and uncertainty quantification for complex multiscale and multiphysics problems. These methods accelerate simulation, optimization, and materials discovery while preserving physical consistency and interpretability. By linking computational mechanics, materials science, and advanced algorithms, we seek to create scalable tools that support faster design exploration, process prediction, and engineering decision-making across a wide range of advanced manufacturing applications for scientific research and practical industrial deployment.

We investigate the simultaneous design of alloy composition and manufacturing process conditions to achieve targeted microstructures, properties, and performance. Rather than treating materials and processing as separate problems, our approach links chemistry, thermal history, phase evolution, defects, and mechanical behavior within an integrated design framework. We combine computational materials modeling, process simulation, data-driven optimization, and experimental characterization to identify promising alloy-process combinations. Particular emphasis is placed on additive manufacturing, where rapid solidification and complex thermal cycles strongly influence material behavior. This research supports the development of new alloys and processing strategies with improved strength, reliability, manufacturability, and application-specific performance requirements.