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Pages

Extreme-Condition Dynamics & Design Lab

Posts

Future Blog Post

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

publications

UniFoil: A Universal Dataset of Airfoils in Transitional and Turbulent Regimes for Subsonic and Transonic Flows

Published in NeurIPS Conference (Dataset and Benchmark Track), 2025

Announces UniFoil, a 500k-sample RANS dataset spanning laminar–turbulent transition through fully turbulent subsonic and transonic regimes to accelerate ML research on realistic aerodynamic flows.

Recommended citation: Kanchi, Rohit Sunil, Benjamin Melanson, Nithin Somasekharan, Shaowu Pan, and Sicheng He. (2025). "UniFoil: A Universal Dataset of Airfoils in Transitional and Turbulent Regimes for Subsonic and Transonic Flows." NeurIPS Dataset and Benchmark Track, October 2025. arXiv:2505.21124. /files/UniFoil_paper.pdf

Modal-Centric Field Inversion via Differentiable Proper Orthogonal Decomposition

Published in Journal of Computational Physics (preprint), 2026

Formulates modal-centric field inversion that matches dominant POD modes instead of raw fields and introduces an adjoint-based differentiable POD operator for scalable inverse design.

Recommended citation: Kanchi, Rohit Sunil, and Sicheng He. (2026). "Modal-Centric Field Inversion via Differentiable Proper Orthogonal Decomposition." Journal of Computational Physics (under review). arXiv:2601.14858. /files/mcfi_paper.pdf