85 citations · 184 across the 6 of their papers we have counts for
6 papers
Chern number reversal and emergent superconductivity in rhombohedral graphene induced by in-plane magnetic fields
Xiaozhou Zan, Hangzhe Li, Jiawei Guo +11
Rhombohedral graphene with topological flat bands offers an ideal platform for realizing correlated and topological quantum phases. Here we investigate hBN aligned eight-layer rhom…
Generative Inversion of Spectroscopic Data for Amorphous Structure Elucidation
Jiawei Guo, Daniel Schwalbe-Koda
Determining atomistic structures from characterization data is one of the most common yet intricate problems in materials science. Particularly in amorphous materials, proposing st…
Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision
Thea Klaeboe Aarrestad, Alaa Abdelhamid, Haider Abidi +457
Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape co…
A practical PINN framework for multi-scale problems with multi-magnitude loss terms
Yong Wang, Yanzhong Yao, Jiawei Guo +1
For multi-scale problems, the conventional physics-informed neural networks (PINNs) face some challenges in obtaining available predictions. In this paper, based on PINNs, we propo…
A deep learning method for multi-material diffusion problems based on physics-informed neural networks
Yanzhong Yao, Jiawei Guo, Tongxiang Gu
Given the facts of the extensiveness of multi-material diffusion problems and the inability of the standard PINN(Physics-Informed Neural Networks) method for such problems, in this…
Pre-training strategy for solving evolution equations based on physics-informed neural networks
Jiawei Guo, Yanzhong Yao, Han Wang +1
The physics informed neural network (PINN) is a promising method for solving time-evolution partial differential equations (PDEs). However, the standard PINN method may fail to sol…