3 papers
cs.LG2026
GAIA: Geometry-Adaptive Operator Learning for Forward and Inverse Problems
Meenakshi Krishnan, Pranav Pulijala, Ke Chen +2
Operator learning for partial differential equations (PDEs) on arbitrary geometries builds fast neural surrogates for large-scale simulation. Although recent geometry-adaptive neur…
cs.GR2025
ViscoReg: Neural Signed Distance Functions via Viscosity Solutions
Meenakshi Krishnan, Ramani Duraiswami
Implicit Neural Representations (INRs) that learn Signed Distance Functions (SDFs) from point cloud data represent the state-of-the-art for geometrically accurate 3D scene reconstr…
cs.GR2025
3D Gaussian Splatting with Normal Information for Mesh Extraction and Improved Rendering
Meenakshi Krishnan, Liam Fowl, Ramani Duraiswami
Differentiable 3D Gaussian splatting has emerged as an efficient and flexible rendering technique for representing complex scenes from a collection of 2D views and enabling high-qu…