Solid Mechanics, Computational Mechanics, and AI with ML
By combining data-driven methodologies with physics-based models, we aim to handle the following research to develop solid mechanics theories, analyses, modeling, and design across various applications in materials and structures to enrich our human life.
Structural Optimization
We pursue structural optimization to design more efficient and resilient structures, minimizing material usage while maximizing performance, which is critical for sustainability and cost-effectiveness in engineering applications.
Reduced-order Modeling
We focus on reduced-order modeling to simplify complex physical systems, enabling faster simulations without sacrificing accuracy, which is vital for real-time applications and large-scale design iterations.
Real-time Simulation
Real-time simulation enables immediate feedback in complex systems, allowing for rapid decision-making and adaptive control in dynamic environments, such as automotive safety systems or aerospace operations.
Uncertainty Quantification
Uncertainty quantification is crucial for assessing the reliability and robustness of models under varying conditions, ensuring that designs can withstand real-world uncertainties and perform as expected across diverse scenarios.