2 citations · 5 across the 8 of their papers we have counts for
8 papers
Generative Discovery of Partial Differential Equations by Learning from Math Handbooks
Hao Xu, Yuntian Chen, Rui Cao +5
Data driven discovery of partial differential equations (PDEs) is a promising approach for uncovering the underlying laws governing complex systems. However, purely data driven tec…
Discovery of Green's function based on symbolic regression with physical hard constraints
Jianghang Gu, Mengge Du, Yuntian Chen +1
The Green's function, serving as a kernel function that delineates the interaction relationships of physical quantities within a field, holds significant research implications acro…
LLM4ED: Large Language Models for Automatic Equation Discovery
Mengge Du, Yuntian Chen, Zhongzheng Wang +2
Equation discovery is aimed at directly extracting physical laws from data and has emerged as a pivotal research domain. Previous methods based on symbolic mathematics have achieve…
A knowledge-based data-driven (KBDD) framework for all-day identification of cloud types using satellite remote sensing
Longfeng Nie, Yuntian Chen, Mengge Du +2
Cloud types, as a type of meteorological data, are of particular significance for evaluating changes in rainfall, heatwaves, water resources, floods and droughts, food security and…
Physics-constrained robust learning of open-form partial differential equations from limited and noisy data
Mengge Du, Yuntian Chen, Longfeng Nie +2
Unveiling the underlying governing equations of nonlinear dynamic systems remains a significant challenge. Insufficient prior knowledge hinders the determination of an accurate can…
DISCOVER: Deep identification of symbolically concise open-form PDEs via enhanced reinforcement-learning
Mengge Du, Yuntian Chen, Dongxiao Zhang
The working mechanisms of complex natural systems tend to abide by concise and profound partial differential equations (PDEs). Methods that directly mine equations from data are ca…
AutoKE: An automatic knowledge embedding framework for scientific machine learning
Mengge Du, Yuntian Chen, Dongxiao Zhang
Imposing physical constraints on neural networks as a method of knowledge embedding has achieved great progress in solving physical problems described by governing equations. Howev…
The USTC-NELSLIP Systems for Simultaneous Speech Translation Task at IWSLT 2021
Dan Liu, Mengge Du, Xiaoxi Li +2
This paper describes USTC-NELSLIP's submissions to the IWSLT2021 Simultaneous Speech Translation task. We proposed a novel simultaneous translation model, Cross Attention Augmented…