1 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
Stochastic Reconfiguration with Warm-Started SVD
Dexuan Zhou, Huajie Chen, Cheuk Hin Ho +2
The combination of the variational Monte Carlo (VMC) method with deep learning wave function architectures has led to several successes in ground-state calculations of quantum many…
Variational Geometry-aware Neural Network based Method for Solving High-dimensional Diffeomorphic Mapping Problems
Zhiwen Li, Cheuk Hin Ho, Lok Ming Lui
Traditional methods for high-dimensional diffeomorphic mapping often struggle with the curse of dimensionality. We propose a mesh-free learning framework designed for -dimension…
Flexible Uncertainty Calibration for Machine-Learned Interatomic Potentials
Cheuk Hin Ho, Christoph Ortner, Yangshuai Wang
Reliable uncertainty quantification (UQ) is essential for developing machine-learned interatomic potentials (MLIPs) in predictive atomistic simulations. Conformal prediction (CP) i…
A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks
Yifan Yu, Cheuk Hin Ho, Yangshuai Wang
Physics-Informed Neural Networks (PINNs) have emerged as a powerful framework for solving PDEs, yet existing uncertainty quantification (UQ) approaches for PINNs generally lack rig…