My research is strongly driven by real world engineering and scientific challenges. Rather than developing computational methods in isolation, I focus on building scalable numerical frameworks, simulation technologies, and software tools that can address practical problems across multiple disciplines. These application areas represent the domains where my work in computational mechanics, high performance computing, optimization, machine learning, and scientific software development can create meaningful scientific, industrial, and societal impact.
Description
Integration of machine learning, surrogate modeling, neural operators, and AI-assisted computational frameworks for predictive engineering simulations and design automation.
My Work in This Area
Working on surrogate-assisted fracture modeling, neural operator frameworks, CNN-based inverse design, AI-driven adaptive mesh refinement, and physics-informed computational workflows.