1 citations · 2 across the 6 of their papers we have counts for
6 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…
Estimating Uncertainty in Landslide Segmentation Models
Savinay Nagendra, Chaopeng Shen, Daniel Kifer
Landslides are a recurring, widespread hazard. Preparation and mitigation efforts can be aided by a high-quality, large-scale dataset that covers global at-risk areas. Such a datas…
PatchRefineNet: Improving Binary Segmentation by Incorporating Signals from Optimal Patch-wise Binarization
Savinay Nagendra, Chaopeng Shen, Daniel Kifer
The purpose of binary segmentation models is to determine which pixels belong to an object of interest (e.g., which pixels in an image are part of roads). The models assign a logit…