Postdoctoral Research Fellow, Johns Hopkins
2024 — PresentDeveloping evolutionary deep neural networks for multi-physics problems in solid mechanics, bridging mechanics and machine learning.
Learn moreI’m Meenu, a Postdoctoral Research Fellow at Johns Hopkins University working at the intersection of computational mechanics and scientific machine learning — building solvers, surrogates, and optimization tools that help us understand how structures and materials really behave.
Physics, analysis, and design — in one workflow.
From phase-field fracture and large-scale topology optimization during my Ph.D. at IIT Roorkee, to evolutionary deep neural networks and neural-operator benchmarks at Johns Hopkins, my work is about making hard mechanics problems solvable at scale.
Computational Mechanics
Scientific ML
Phase Field Fracture
Topology Optimization
HPC & FEniCS
Contact
I’m always happy to discuss research ideas, collaborations, or opportunities. Reach me at ukrishn4@jh.edu or fill in the form below.
Milestones
Recent Achievements
Fracture-Bench: a benchmark for neural operators
Johns Hopkins · OngoingShared datasets and consistent evaluation so neural-operator surrogates for fracture can finally be compared on equal footing.
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Large-scale topology optimization, then 3D printed
Ph.D. · IIT RoorkeeScaled optimization to large 3D problems in FEniCS with MPI parallelism, then printed the designs to check they really carry their load.
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Adaptive phase-field fracture with automatic time stepping
Ph.D. · IIT RoorkeeEnergy-based error indicators drive mesh refinement, keeping fast crack growth accurate without meshing the whole domain finely.
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Auxetic metamaterials by design
Ph.D. · IIT RoorkeeDesigning material layouts with negative Poisson’s ratio through simulation, optimization, and fabrication.
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Ph.D. in Computational Mechanics, IIT Roorkee
Doctoral researchEfficient algorithms for phase-field fracture and 3D-printable topology optimization: mesh adaptivity, time stepping, and parallel HPC.
Learn moreSelected work
Research Highlights
01
Evolutionary Deep Neural Networks
Mesh-free, physics-informed solvers that evolve network parameters in time to march coupled physics forward.
Read the project 02Phase Field Fracture & Mesh Adaptivity
Cracks as a smooth damage field, with refinement guided by an energy-based error indicator instead of a uniformly fine mesh.
Read the project 03Large Scale Topology Optimization
Letting the optimizer decide where material goes — scaled to large 3D problems and validated by printing the result.
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