A computer program for uncertainty analysis integrating regression and Bayesian methods. - Dan Lu 001; Ming Ye; Mary C. Hill; Eileen P. Poeter; Gary P. Curtis (Environ. Model. Softw., 2014)

An Efficient Bayesian Method for Advancing the Application of Deep Learning in Earth Science. - Dan Lu 001; Siyan Liu; Daniel M. Ricciuto (ICDM Workshops, 2019)

Learning-Based Inversion-Free Model-Data Integration to Advance Ecosystem Model Prediction. - Dan Lu 001; Daniel M. Ricciuto (ICDM Workshops, 2019)

Efficient surrogate modeling methods for large-scale Earth system models based on machine learning techniques. - Dan Lu 001; Daniel M. Ricciuto (CoRR, 2019)

An adaptive Kriging surrogate method for efficient uncertainty quantification with an application to geological carbon sequestration modeling. - Shaoxing Mo; Xiaoqing Shi; Dan Lu 001; Ming Ye; Jichun Wu (Comput. Geosci., 2019)

Efficient Distance-based Global Sensitivity Analysis for Terrestrial Ecosystem Modeling. - Dan Lu 001; Daniel M. Ricciuto (ICDM (Workshops), 2020)

A Scalable Evolution Strategy with Directional Gaussian Smoothing for Blackbox Optimization. - Jiaxin Zhang 005; Hoang Tran; Dan Lu 001; Guannan Zhang (CoRR, 2020)

Enabling long-range exploration in minimization of multimodal functions. - Jiaxin Zhang 005; Hoang Tran; Dan Lu 001; Guannan Zhang (UAI, 2021)

PI3NN: Prediction intervals from three independently trained neural networks. - Siyan Liu; Pei Zhang; Dan Lu 001; Guannan Zhang (CoRR, 2021)

Exploiting the Local Parabolic Landscapes of Adversarial Losses to Accelerate Black-Box Adversarial Attack. - Hoang Tran; Dan Lu 001; Guannan Zhang (ECCV (5), 2022)

Identifying Hydrometeorological Factors Influencing Reservoir Releases Using Machine Learning Methods. - Ming Fan; Lujun Zhang; Siyan Liu; Tiantian Yang; Dan Lu 001 (ICDM (Workshops), 2022)

Improving net ecosystem CO2 flux prediction using memory-based interpretable machine learning. - Siyan Liu; Dan Lu 001; Daniel M. Ricciuto; Anthony P. Walker (ICDM (Workshops), 2022)

PI3NN: Out-of-distribution-aware Prediction Intervals from Three Neural Networks. - Siyan Liu; Pei Zhang; Dan Lu 001; Guannan Zhang (ICLR, 2022)

Time series anomaly detection in power electronics signals with recurrent and ConvLSTM autoencoders. - Majdi I. Radaideh; Chris Pappas; Jared Walden; Dan Lu 001; Lasitha Vidyaratne; Thomas Britton; Kishansingh Rajput; Malachi Schram; Sarah Cousineau (Digit. Signal Process., 2022)

Accelerating Scientific Simulations with Bi-Fidelity Weighted Transfer Learning. - Katarzyna Borowiec; Dan Lu 001; Vikas Chandan; Samrat Chatterjee; Pradeep Ramuhalli; Ramakrishna Tipireddy; Mahantesh Halappanavar; Frank Liu 001 (ICMLA, 2023)

Multi-module based CVAE to predict HVCM faults in the SNS accelerator. - Yasir Alanazi; Malachi Schram; Kishansingh Rajput; Steven Goldenberg; Lasitha Vidyaratne; Chris Pappas; Majdi I. Radaideh; Dan Lu 001; Pradeep Ramuhalli; Sarah Cousineau (CoRR, 2023)

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. - Shuaiwen Leon Song; Bonnie Kruft; Minjia Zhang; Conglong Li; Shiyang Chen 004; Chengming Zhang 006; Masahiro Tanaka; Xiaoxia Wu; Jeff Rasley; Ammar Ahmad Awan; Connor Holmes; Martin Cai; et al. (CoRR, 2023)

Explainable machine learning model for multi-step forecasting of reservoir inflow with uncertainty quantification. - Ming Fan; Siyan Liu; Dan Lu 001; Sudershan Gangrade; Shih-Chieh Kao (Environ. Model. Softw., 2023)

ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability. - Xiao Wang 004; Siyan Liu; Aristeidis Tsaris; Jong-Youl Choi; Ashwin M. Aji; Ming Fan; Wei Zhang 261; Junqi Yin; Moetasim Ashfaq; Dan Lu 001; Prasanna Balaprakash (SC, 2024)

A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics. - Junqi Yin; Siming Liang; Siyan Liu; Feng Bao 005; Hristo G. Chipilski; Dan Lu 001; Guannan Zhang (SC Workshops, 2024)

Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation. - Minglei Yang 005; Pengjun Wang; Ming Fan; Dan Lu 001; Yanzhao Cao; Guannan Zhang (CoRR, 2024)

ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability. - Xiao Wang 004; Aristeidis Tsaris; Siyan Liu; Jong-Youl Choi; Ming Fan; Wei Zhang 261; Junqi Yin; Moetasim Ashfaq; Dan Lu 001; Prasanna Balaprakash (CoRR, 2024)

Sequence Length Scaling in Vision Transformers for Scientific Images on Frontier. - Aristeidis Tsaris; Chengming Zhang 006; Xiao Wang 004; Junqi Yin; Siyan Liu; Moetasim Ashfaq; Ming Fan; Jong-Youl Choi; Mohamed Wahib; Dan Lu 001; Prasanna Balaprakash; Fei-Yue Wang 001 (CoRR, 2024)

GenAI4UQ: A Software for Inverse Uncertainty Quantification Using Conditional Generative Models. - Ming Fan; Zezhong Zhang; Dan Lu 001; Guannan Zhang (CoRR, 2024)

Distance preserving machine learning for uncertainty aware accelerator capacitance predictions. - Steven Goldenberg; Malachi Schram; Kishansingh Rajput; Thomas Britton; Chris Pappas; Dan Lu 001; Jared Walden; Majdi I. Radaideh; Sarah Cousineau; Sudarshan Harave (Mach. Learn. Sci. Technol., 2024)

GenAI4UQ: A software for forward and inverse uncertainty quantification using conditional generative AI. - Ming Fan; Zezhong Zhang; Dan Lu 001; Guannan Zhang (SoftwareX, 2025)

ORBIT-2: Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling. - Xiao Wang 004; Jong-Youl Choi; Takuya Kurihaya; Isaac Lyngaas; Hong-Jun Yoon; Ming Fan; Nasik Muhammad Nafi; Aristeidis Tsaris; Ashwin M. Aji; Maliha Hossain; Mohamed Wahib; Dali Wang; et al. (CoRR, 2025)

Diffusion-based supervised learning of generative models for efficient sampling of multimodal distributions. - Hoang Tran; Zezhong Zhang; Feng Bao 005; Dan Lu 001; Guannan Zhang (CoRR, 2025)

Distributed Cross-Channel Hierarchical Aggregation for Foundation Models. - Aristeidis Tsaris; Isaac Lyngaas; John Lagregren; Mohamed Wahib; Larry M. York; Prasanna Balaprakash; Dan Lu 001; Yi-Fei Wang; Xiao Wang 004 (CoRR, 2025)