4 papers
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…