5 papers
GENIE: Gram-Eigenmode INR Editing with Closed-Form Geometry Updates
Samundra Karki, Adarsh Krishnamurthy, Baskar Ganapathysubramanian
Implicit Neural Representations (INRs) provide compact models of geometry, but it is unclear when their learned shapes can be edited without retraining. We show that the Gram opera…
Neural Geometry for PDEs: Regularity, Stability, and Convergence Guarantees
Samundra Karki, Adarsh Krishnamurthy, Baskar Ganapathysubramanian
Implicit Neural Representations (INRs) have emerged as a powerful tool for geometric representation, yet their suitability for physics-based simulation remains underexplored. While…
Mechanics Simulation with Implicit Neural Representations of Complex Geometries
Samundra Karki, Ming-Chen Hsu, Adarsh Krishnamurthy +1
Implicit Neural Representations (INRs), characterized by neural network-encoded signed distance fields, provide a powerful means to represent complex geometries continuously and ef…
Direct Flow Simulations with Implicit Neural Representation of Complex Geometry
Samundra Karki, Mehdi Shadkah, Cheng-Hau Yang +4
Implicit neural representations have emerged as a powerful approach for encoding complex geometries as continuous functions. These implicit models are widely used in computer visio…
FlowBench: A Large Scale Benchmark for Flow Simulation over Complex Geometries
Ronak Tali, Ali Rabeh, Cheng-Hau Yang +10
Simulating fluid flow around arbitrary shapes is key to solving various engineering problems. However, simulating flow physics across complex geometries remains numerically challen…