Search Results for author:
Found 6 papers, 1 papers with code
Deep decomposition method for the limited aperture inverse obstacle scattering problem
It is well known that traditional deep learning relies solely on data, which may limit its performance for the inverse problem when only indirect observation data and a physical model are available.
Adaptive operator learning for infinite-dimensional Bayesian inverse problems
To this end, we develop an adaptive operator learning framework that can reduce modeling error gradually by forcing the surrogate to be accurate in local areas.
UNREAL:Unlabeled Nodes Retrieval and Labeling for Heavily-imbalanced Node Classification
To select which unlabeled nodes to add, we propose geometric ranking to rank unlabeled nodes.
Failure-informed adaptive sampling for PINNs, Part II: combining with re-sampling and subset simulation
The second extension is to present the subset simulation algorithm as the posterior model (instead of the truncated Gaussian model) for estimating the error indicator, which can more effectively estimate the failure probability and generate new effective training points in the failure region.
Failure-informed adaptive sampling for PINNs
For instance, a fixed set of (prior chosen) training points may fail to capture the effective solution region (especially for problems with singularities).
Identifying the module structure of swarms using a new framework of network-based time series clustering
In summary, the proposed framework is a flexible and scalable time series clustering method that can solve various time series clustering problems especially the trajectory clustering of the UAV swarm and has great potential for general time series analysis.
