10 citations · 14 across the 12 of their papers we have counts for
5 papers · 1 filter
Improving Accuracy and Efficiency of Implicit Neural Representations: Making SIREN a WINNER
Hemanth Chandravamsi, Dhanush V. Shenoy, Steven H. Frankel
We identify and address a fundamental limitation of sinusoidal representation networks (SIRENs), a class of implicit neural representations. SIRENs Sitzmann et al. (2020), when not…
Spectral Bottleneck in Sinusoidal Representation Networks: Noise is All You Need
Hemanth Chandravamsi, Dhanush V. Shenoy, Itay Zinn +3
This work identifies and attempts to address a fundamental limitation of implicit neural representations with sinusoidal activation. The fitting error of SIRENs is highly sensitive…
Quantum Physics-Informed Neural Networks for Maxwell's Equations: Circuit Design, "Black Hole" Barren Plateaus Mitigation, and GPU Acceleration
Ziv Chen, Gal G. Shaviner, Hemanth Chandravamsi +3
Physics-Informed Neural Networks (PINNs) have emerged as a promising approach for solving partial differential equations (PDEs) by embedding the governing physics into the loss fun…
Deep Learning vs. Black-Scholes: Option Pricing Performance on Brazilian Petrobras Stocks
Joao Felipe Gueiros, Hemanth Chandravamsi, Steven H. Frankel
This paper explores the use of deep residual networks for pricing European options on Petrobras, one of the world's largest oil and gas producers, and compares its performance with…
PINNs for Solving Unsteady Maxwell's Equations: Convergence Issues and Comparative Assessment with Compact Schemes
Gal G. Shaviner, Hemanth Chandravamsi, Shimon Pisnoy +2
Physics-Informed Neural Networks (PINNs) have recently emerged as a promising alternative for solving partial differential equations, offering a mesh-free framework that incorporat…