I’m U. Meenu Krishnan, a researcher who firmly believes that you should either love what you do, or change it — because passion fuels purpose, and purpose drives excellence.

I am currently working as a Postdoctoral Research Fellow at Johns Hopkins University, where my research focuses on Evolutionary Deep Neural Networks (EDNN). I am using EDNN to solve multi-physics problems in solid mechanics, such as coupled fracture, heat conduction, and wave propagation. My goal is to combine the strengths of physics-based modeling and machine learning to create accurate and efficient solvers for challenging engineering problems.

My research journey began in January 2019, when I joined the Computational Mechanics Lab as a Ph.D. scholar. My work focused on the phase field method for modeling fracture and performing topology optimization. I developed computationally efficient algorithms by introducing mesh adaptivity and automatic time stepping, allowing us to solve large and realistic problems faster and more accurately. I also explored parallel computing techniques to scale up these simulations on high-performance computing systems.

Later, I extended my research to large-scale topology optimization, aiming to find the best material layouts for structures while considering fracture, manufacturing constraints, and performance. This work helped in developing practical tools for designing optimized components for real-world applications.

Outside the lab, I love to spend time drawing and simply sitting in peace. These quiet moments help me recharge and bring clarity to my thoughts, often inspiring fresh ideas in both life and research.