Accelerating dissipative particle dynamics simulations on GPUs: Algorithms, numerics and applications. - Yu-Hang Tang; George E. Karniadakis (Comput. Phys. Commun., 2014)

Energy-conserving dissipative particle dynamics with temperature-dependent properties. - Zhen Li 003; Yu-Hang Tang; Huan Lei; Bruce Caswell; George E. Karniadakis (J. Comput. Phys., 2014)

The in-silico lab-on-a-chip: petascale and high-throughput simulations of microfluidics at cell resolution. - Diego Rossinelli; Yu-Hang Tang; Kirill Lykov; Dmitry Alexeev; Massimo Bernaschi; Panagiotis E. Hadjidoukas; Mauro Bisson; Wayne Joubert; Christian Conti; George E. Karniadakis; Massimiliano Fatica; Igor Pivkin; et al. (SC, 2015)

Mesoscale modeling of phase transition dynamics of thermoresponsive polymers. - Zhen Li 003; Yu-Hang Tang; Xuejin Li; George E. Karniadakis (CoRR, 2015)

Multiscale Universal Interface: A concurrent framework for coupling heterogeneous solvers. - Yu-Hang Tang; Shuhei Kudo; Xin Bian; Zhen Li 003; George E. Karniadakis (J. Comput. Phys., 2015)

Identifying micro-inversions using high-throughput sequencing reads. - Feifei He; Yang Li; Yu-Hang Tang; Jian Ma 004; Huaiqiu Zhu (BMC Genom., 2016)

OpenRBC: A Fast Simulator of Red Blood Cells at Protein Resolution. - Yu-Hang Tang; Lu Lu 010; He Li; Constantinos Evangelinos; Leopold Grinberg; Vipin Sachdeva; George E. Karniadakis (CoRR, 2017)

An Atomistic Fingerprint Algorithm for Learning Ab Initio Molecular Force Fields. - Yu-Hang Tang; Dongkun Zhang; George E. Karniadakis (CoRR, 2017)

GPU-accelerated red blood cells simulations with transport dissipative particle dynamics. - Ansel L. Blumers; Yu-Hang Tang; Zhen Li 003; Xuejin Li; George E. Karniadakis (Comput. Phys. Commun., 2017)

Prediction of Atomization Energy Using Graph Kernel and Active Learning. - Yu-Hang Tang; Wibe A. de Jong (CoRR, 2018)

A dissipative particle dynamics method for arbitrarily complex geometries. - Zhen Li 003; Xin Bian; Yu-Hang Tang; George E. Karniadakis (J. Comput. Phys., 2018)

A High-Throughput Solver for Marginalized Graph Kernels on GPU. - Yu-Hang Tang; Oguz Selvitopi; Doru-Thom Popovici; Aydin Buluç (IPDPS, 2020)

A GPU-accelerated package for simulation of flow in nanoporous source rocks with many-body dissipative particle dynamics. - Yidong Xia; Ansel L. Blumers; Zhen Li 003; Lixiang Luo; Yu-Hang Tang; Joshua Kane; Jan Goral; Hai Huang; Milind Deo; Matthew Andrew (Comput. Phys. Commun., 2020)

Detecting Label Noise via Leave-One-Out Cross-Validation. - Yu-Hang Tang; Yuanran Zhu; Wibe A. de Jong (CoRR, 2021)

Nonlinear Matrix Approximation with Radial Basis Function Components. - Elizaveta Rebrova; Yu-Hang Tang (CoRR, 2021)

Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery. - Peiyuan Gao; Xiu Yang; Yu-Hang Tang; Muqing Zheng; Amity Anderson; Vijayakumar Murugesan; Aaron Hollas; Wei Wang (CoRR, 2021)

A Comparative Study of Marginalized Graph Kernel and Message-Passing Neural Network. - Yan Xiang; Yu-Hang Tang; Guang Lin 001; Huai Sun (J. Chem. Inf. Model., 2021)

Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation. - Yan Xiang; Yu-Hang Tang; Zheng Gong; Hongyi Liu; Liang Wu; Guang Lin 001; Huai Sun (CoRR, 2022)

Learning stochastic dynamics with statistics-informed neural network. - Yuanran Zhu; Yu-Hang Tang; Changho Kim (J. Comput. Phys., 2023)

Interpretable Molecular Property Predictions Using Marginalized Graph Kernels. - Yan Xiang; Yu-Hang Tang; Guang Lin 001; Daniel Reker (J. Chem. Inf. Model., 2023)

Efficient Exploration of Chemical Compound Space Using Active Learning for Prediction of Thermodynamic Properties of Alkane Molecules. - Yan Xiang; Yu-Hang Tang; Zheng Gong; Hongyi Liu; Liang Wu; Guang Lin 001; Huai Sun (J. Chem. Inf. Model., 2023)