2 citations · 2 across the 5 of their papers we have counts for
10 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. Uncertaint…
Hybrid Quantum-Classical PINNs for Scientific Computing: A Multi-GPU Open-Source Framework
Shimon Pisnoy, Hemanth Chandravamsi, Ziv Chen +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…
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…
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…
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…