Source Themes

GAIA: Geometry Adaptive Integral AutoEncoder Networks for Operator Learning
Operator learning for partial differential equations (PDEs) on arbitrary geometries builds fast neural surrogates for large-scale …
Meenakshi Krishnan, P. Pulijala, Ke Chen, Haizhao Yang, Ramani Duraiswami
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Generalization Analysis and Improved Shape Representation with Neural Signed Distance Functions
Meenakshi Krishnan, Ramani Duraiswami
ViscoReg: Neural Signed Distance Functions via Viscosity Solutions
Meenakshi Krishnan, Ramani Duraiswami
Error Analysis for Learning Time-Stepping Algorithms for PDEs
Deep neural networks (DNNs) have recently emerged as effective tools for approximating solution operators of partial differential …
Ke Chen, Meenakshi Krishnan, Haizhao Yang
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3D Gaussian Splatting with Normal Information for Mesh Extraction and Improved Rendering
Differentiable 3D Gaussian splatting has emerged as an efficient and flexible rendering technique for representing complex scenes from …
Meenakshi Krishnan, Liam Fowl, Ramani Duraiswami
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A unified asymptotic preserving and well-balanced scheme for the Euler system with multiscale relaxation
The design and analysis of a unified asymptotic preserving (AP) and well-balanced scheme for the Euler Equations with gravitational and …
Meenakshi Krishnan, K R Arun, Saurav Samantaray
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