一、基本情況
姓名：涂勁之
性別：男
學(xué)歷學(xué)位：博士
職稱：講師/碩導(dǎo)
研究方向：地質(zhì)災(zāi)害防治、人工智能、計算力學(xué)
電子郵箱：tujingzhi@csust.edu.cn
二、項目
(1)降雨條件下湘西北紅層斜坡地質(zhì)災(zāi)害成災(zāi)機理與易發(fā)性人工智能預(yù)警系統(tǒng)研究
(2)基于人工智能大模型的個人知識庫問答系統(tǒng)開發(fā)
三、代表性論文
1. Jingzhi Tu; Chun Liu; Pian. Qi. Physics-informed Neural Network Integrating PointNet-based Adaptive Refinement for Investigating Crack Propagation in Industrial Applications. IEEE Transactions on Industrial Informatics. 19(2): 2210-2218 (2023). (IF=12.3，中科院一區(qū))
2. Jingzhi Tu; Nengxiong Xu; Gang Mei. A peridynamics modeling approach for pre-cracked rock cracking processes under impact by integrating Drucker-Prager plasticity model and efficient contact model. Journal of Rock Mechanics and Geotechnical Engineering. PP: 1-20 (2025). (IF=9.4，中科院一區(qū))
3. Jingzhi Tu; Gang Mei; Francesco Piccialli. An Improved Nystr?m Spectral Graph Clustering Using k-core Decomposition as a Sampling Strategy for Large Networks. Journal of King Saud University - Computer and Information Sciences. 34(6): 3673-3684 (2022). (IF=6.9，中科院二區(qū))
4. Jingzhi Tu; Gang Mei; Francesco Piccialli. An Efficient Deep Learning Approach Using Improved Generative Adversarial Networks for Incomplete Information Completion of Self-driving Vehicles. Journal of Grid Computing. 20, 21 (2022). (IF=5.5，中科院二區(qū))
5. Jingzhi Tu; Gang Mei; Zhengjing Ma; Francesco Piccialli. SWCGAN: Generative Adversarial Network Combining Swin Transformer and CNN for Remote Sensing Image Super-Resolution. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15: 5662-5673 (2022). (IF=5.5，中科院二區(qū))
歡迎有一定編程基礎(chǔ)，或?qū)τ谌斯ぶ悄芘c工程軟件開發(fā)感興趣的同學(xué)聯(lián)系，在新領(lǐng)域一起研究探討。
