3 papers
stat.ML2025
Uncertainty Quantification for Physics-Informed Neural Networks with Extended Fiducial Inference
Frank Shih, Zhenghao Jiang, Faming Liang
Uncertainty quantification (UQ) in scientific machine learning is increasingly critical as neural networks are widely adopted to tackle complex problems across diverse scientific d…
cs.LG2023
Uncertainty Quantification of Deep Learning for Spatiotemporal Data: Challenges and Opportunities
Wenchong He, Zhe Jiang
With the advancement of GPS, remote sensing, and computational simulations, large amounts of geospatial and spatiotemporal data are being collected at an increasing speed. Such eme…
cs.LG2023
Deep Learning for Spatiotemporal Big Data: A Vision on Opportunities and Challenges
Zhe Jiang
With advancements in GPS, remote sensing, and computational simulation, an enormous volume of spatiotemporal data is being collected at an increasing speed from various application…