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Found 7 papers, 0 papers with code
LanePtrNet: Revisiting Lane Detection as Point Voting and Grouping on Curves
from object detection and segmentation tasks, while these approaches require manual adjustments for curved objects, involve exhaustive searches on predefined anchors, require complex post-processing steps, and may lack flexibility when applied to real-world scenarios. In this paper, we propose a novel approach, LanePtrNet, which treats lane detection as a process of point voting and grouping on ordered sets: Our method takes backbone features as input and predicts a curve-aware centerness, which represents each lane as a point and assigns the most probable center point to it.
Uncertainty quantification for deeponets with ensemble kalman inversion
Finally, we demonstrate the effectiveness and versatility of our proposed methodology across various benchmark problems, showcasing its potential to address the pressing challenges of uncertainty quantification in DeepONets, especially for practical applications with limited and noisy data.
Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion
Bayesian Physics Informed Neural Networks (B-PINNs) have gained significant attention for inferring physical parameters and learning the forward solutions for problems based on partial differential equations.
Physics-informed machine learning
Uncertainty Quantification
Asymptotic-Preserving Neural Networks for multiscale hyperbolic models of epidemic spread
To allow the neural network to operate uniformly with respect to the small scales, it is desirable that the neural network satisfies an Asymptotic-Preservation (AP) property in the learning process.
Nonnegativity-Enforced Gaussian Process Regression
Gaussian Process (GP) regression is a flexible non-parametric approach to approximate complex models.
Bifidelity data-assisted neural networks in nonintrusive reduced-order modeling
In this paper, we present a new nonintrusive reduced basis method when a cheap low-fidelity model and expensive high-fidelity model are available.
When Bifidelity Meets CoKriging: An Efficient Physics-Informed Multifidelity Method
In this work, we propose a framework that combines the approximation-theory-based multifidelity method and Gaussian-process-regression-based multifidelity method to achieve data-model convergence when stochastic simulation models and sparse accurate observation data are available.
