1 citations · 1 across the 4 of their papers we have counts for
4 papers
Joint Spatiotemporal Spectral Neural Operators for Learning PDEs on Irregular Domains
Abdolmehdi Behroozi, Chaopeng Shen
Learning solution operators for partial differential equations (PDEs) on irregular and geometry-dependent domains remains a central challenge in scientific machine learning. While…
Sensitivity-Constrained Neural Operators for Data-Efficient Forward and Inverse Modeling of Partial Differential Equation Systems
Abdolmehdi Behroozi, Chaopeng Shen, Daniel Kifer +1
Neural operators provide fast surrogates for partial differential equation (PDE) solvers, but their reliability can degrade for high-dimensional spatial inputs and inverse or repea…
Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations
Abdolmehdi Behroozi, Chaopeng Shen and, Daniel Kifer
Parametric differential equations of the form du/dt = f(u, x, t, p) are fundamental in science and engineering. While deep learning frameworks such as the Fourier Neural Operator (…
SAMIC: Segment Anything with In-Context Spatial Prompt Engineering
Savinay Nagendra, Kashif Rashid, Chaopeng Shen +1
Few-shot segmentation is the problem of learning to identify specific types of objects (e.g., airplanes) in images from a small set of labeled reference images. The current state o…