10 citations · 14 across the 12 of their papers we have counts for
12 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…
Feature-preserving Latent-EnKF for Data Assimilation of Flows with Shocks
Hemanth Chandravamsi, Hangchuan Hu, Ponkrshnan Thiagarajan +1
The ensemble Kalman filter (EnKF) is widely adopted for sequential data assimilation, but fails for solutions with discontinuities, such as shocks in compressible flows. For ensemb…
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…
On the Application of Gradient Based Reconstruction for Flow Simulations on Generalized Curvilinear and Dynamic Mesh Domains
Hemanth Chandravamsi, Amareshwara Sainadh Chamarthi, Natan Hoffmann +1
Accurate high-speed flow simulations of practical interest require numerical methods with high-resolution properties. In this paper, we present an extension and demonstration of th…
Control of bow shock induced three-dimensional separation using bleed through holes
Hemanth Chandravamsi, Sourabh Bhardwaj, K. Ramachandra +1
The unsteady three-dimensional separated flow on a wall induced by a square protrusion (approximately twice the local boundary layer thickness in width and height), and its control…