Pinned Repositories
BCNN4GRACE
A Bayesian Convolutional Neural Network for reconstructing GRACE TWSA signals
Codes-of-TEAD
This is the codes and examples described in "Mo, S., Lu, D., Shi, X., Zhang, G., Ye, M., Wu, J., & Wu, J. (2017). A Taylor expansion‐based adaptive design strategy for global surrogate modeling with applications in groundwater modeling. Water Resources Research, 53, 10,802–10,823. https://doi.org/10.1002/2017WR021622"
dcedn-gcs
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
Deep-autoregressive-neural-networks-for-dynamical-solute-transport-models
DL-para
Deep learning-based parameterization of heterogeneous geological parameter fields
DL4TWSA
A deep learning model for predicting terrestrial water storage anomalies (TWSA) derived from GRACE and GRACE-FO data
File4Geofluids
Input files for FLOLA-Voronoi
mGstat
Nonstationary_Transformers
Code release for "Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting" (NeurIPS 2022), https://arxiv.org/abs/2205.14415
XBTimesNet
njujinchun's Repositories
njujinchun/BCNN4GRACE
A Bayesian Convolutional Neural Network for reconstructing GRACE TWSA signals
njujinchun/Codes-of-TEAD
This is the codes and examples described in "Mo, S., Lu, D., Shi, X., Zhang, G., Ye, M., Wu, J., & Wu, J. (2017). A Taylor expansion‐based adaptive design strategy for global surrogate modeling with applications in groundwater modeling. Water Resources Research, 53, 10,802–10,823. https://doi.org/10.1002/2017WR021622"
njujinchun/dcedn-gcs
Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
njujinchun/Deep-autoregressive-neural-networks-for-dynamical-solute-transport-models
njujinchun/XBTimesNet
njujinchun/DL-para
Deep learning-based parameterization of heterogeneous geological parameter fields
njujinchun/DL4TWSA
A deep learning model for predicting terrestrial water storage anomalies (TWSA) derived from GRACE and GRACE-FO data
njujinchun/File4Geofluids
Input files for FLOLA-Voronoi
njujinchun/mGstat
njujinchun/Nonstationary_Transformers
Code release for "Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting" (NeurIPS 2022), https://arxiv.org/abs/2205.14415
njujinchun/pCNN4GCS
A probabilistic convolutional neural network for surrogate modeling of GCS models
njujinchun/NSFC-application-template-latex
国家自然科学基金申请书正文（面上项目）LaTeX 模板（非官方）