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Found 6 papers, 3 papers with code
Topological Analysis of Mouse Brain Vasculature via 3D Light-sheet Microscopy Images
In this paper, we study the morphology of brain vasculature through a topological lens.
Preconditioning for Physics-Informed Neural Networks
Physics-informed neural networks (PINNs) have shown promise in solving various partial differential equations (PDEs).
Learning to Segment from Noisy Annotations: A Spatial Correction Approach
However, segmentation label noise usually has strong spatial correlation and has prominent bias in distribution.
PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs
In addition to providing a standardized means of assessing performance, PINNacle also offers an in-depth analysis to guide future research, particularly in areas such as domain decomposition methods and loss reweighting for handling multi-scale problems and complex geometry.
MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks
Physics-informed Neural Networks (PINNs) have recently achieved remarkable progress in solving Partial Differential Equations (PDEs) in various fields by minimizing a weighted sum of PDE loss and boundary loss.
On the Convergence of Optimizing Persistent-Homology-Based Losses
Topological loss based on persistent homology has shown promise in various applications.
