2 citations · 3 across the 11 of their papers we have counts for
11 papers
Supersonic jet impingement on concave surfaces
Hemanth Chandravamsi, Dhanush Vittal Shenoy, Steven H. Frankel
The aeroacoustic resonance of round supersonic jets impinging on concave surfaces is investigated using compressible large-eddy simulations, vortex-sheet modelling, and Powell's fe…
Dynamic masking for boundary-aware velocity reconstruction in volumetric particle tracking with moving solids
Jibu Tom Jose, Arieh Jacobson, Dhanush Vittal Shenoy +2
Volumetric particle tracking velocimetry (PTV) produces scattered Lagrangian tracks that must be reconstructed on an Eulerian grid before velocity gradients, pressure, or hydrodyna…
Gaussian Field Representations for Turbulent Flow: Compression, Scale Separation, and Physical Fidelity
Dhanush Vittal Shenoy, Steven H. Frankel
Representing turbulent flow fields in a compact yet physically faithful form remains a central challenge in computational fluid dynamics. We propose a continuous parametric represe…
Hybrid Quantum-Classical PINNs for Scientific Computing: A Multi-GPU Open-Source Framework
Ziv Chen, Hemanth Chandravamsi, Shimon Pisnoy +4
We present QPINNACLE, an open-source computational framework for physics-informed neural networks (PINNs) that integrates modern training strategies, multi-GPU acceleration, and hy…
Shock propagation through a local constriction
Raz Heppner, Hemanth Chandravamsi, Yoav Gichon +2
The interaction of a shock wave with a localized constriction in a straight conduit is investigated by systematically varying the blockage ratio in the range 0.35-0.75, the normali…
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…
High Resolution Optimized High-Order Schemes for Discretization of Non-Linear Straight and Mixed Second Derivative Terms
Hemanth Chandravamsi, Steven H. Frankel
In this paper, we propose a new set of midpoint-based high-order discretization schemes for computing straight and mixed nonlinear second derivative terms that appear in the compre…