9 papers
Bridging Ab Initio Symmetries and Global Nuclear Masses with Interpretable Neural Networks
Phong Dang, Evander Espinoza, Xiaoliang Wan +6
Ab initio modeling has established Wigner's SU(4) and Elliott's SU(3) as dominant symmetries of the nuclear force in light and intermediate-mass nuclei. We ask whether they also go…
Deep Adaptive Dimension Reduction for Bayesian Inference in Inverse Problems
Yueyang Wang, Xili Wang, Kejun Tang +3
Solving high-dimensional PDE-governed inverse problems is often challenging due to complex non-Gaussian posterior distributions, expensive forward model evaluations, and misspecifi…
FLUID: Flow-based Unified Inference for Dynamics
Tiangang Cui, Xiaodong Feng, Chenlong Pei +2
Bayesian filtering and smoothing for high-dimensional nonlinear dynamical systems are fundamental yet challenging problems in many areas of science and engineering. In this work, w…
Mutual Information Collapse Explains Disentanglement Failure in -VAEs
Minh Vu, Xiaoliang Wan, Shuangqing Wei
The -VAE is a foundational framework for unsupervised disentanglement, using to regulate the trade-off between latent factorization and reconstruction fidelity. Empiricall…
Moving sample method for solving time-dependent partial differential equations
Beining Xu, Haijun Yu, Jiayu Zhai +2
Solving time-dependent partial differential equations (PDEs) that exhibit sharp gradients or local singularities is computationally demanding, as traditional physics-informed neura…
Overcoming Spectral Bias via Cross-Attention
Xiaodong Feng, Tao Tang, Xiaoliang Wan +1
Spectral bias implies an imbalance in training dynamics, whereby high-frequency components may converge substantially more slowly than low-frequency ones. To alleviate this issue,…