4 papers
How Does Distribution Shift Shape Pretraining Gains in Neural PDE Surrogates?
Pochinapeddi Sai Bhargav, Nithin Somasekharan, Rohit Sunil Kanchi +2
Pretraining a neural PDE surrogate can reduce the amount of new CFD data needed when geometry or modeled physics changes. However, it remains unclear how different components of di…
Modal-Centric Field Inversion via Differentiable Proper Orthogonal Decomposition
Rohit Sunil Kanchi, Sicheng He
Inverse problems in computational physics often require matching high-dimensional spatio-temporal fields, leading to prohibitive computational costs and ill-conditioned optimizatio…
UniFoil: A Universal Dataset of Airfoils in Transitional and Turbulent Regimes for Subsonic and Transonic Flows
Rohit Sunil Kanchi, Benjamin Melanson, Nithin Somasekharan +2
We present UniFoil, a large publicly available universal airfoil dataset based on Reynolds-averaged Navier-Stokes (RANS) simulations. It contains over 500,000 samples spanning a wi…
Differentiable Singular Value Decomposition
Rohit Kanchi, Sicheng He
Singular value decomposition is widely used in modal analysis, such as proper orthogonal decomposition and resolvent analysis, to extract key features from complex problems. SVD de…