A scalable interface-resolved simulation of particle-laden flow using the lattice Boltzmann method. - Nicholas Geneva; Cheng Peng 005; Xiaoming Li 010; Lian-Ping Wang (Parallel Comput., 2017)

Direct numerical simulation of turbulent pipe flow using the lattice Boltzmann method. - Cheng Peng 005; Nicholas Geneva; Zhaoli Guo; Lian-Ping Wang (J. Comput. Phys., 2018)

A lattice-Boltzmann scheme of the Navier-Stokes equation on a three-dimensional cuboid lattice. - Lian-Ping Wang; Haoda Min; Cheng Peng 005; Nicholas Geneva; Zhaoli Guo (Comput. Math. Appl., 2019)

Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks. - Nicholas Geneva; Nicholas Zabaras (J. Comput. Phys., 2019)

Multi-fidelity Generative Deep Learning Turbulent Flows. - Nicholas Geneva; Nicholas Zabaras (CoRR, 2020)

Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks. - Nicholas Geneva; Nicholas Zabaras (J. Comput. Phys., 2020)

Transformers for modeling physical systems. - Nicholas Geneva; Nicholas Zabaras (Neural Networks, 2022)

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation. - Sungduk Yu; Walter M. Hannah; Liran Peng; Jerry Lin; Mohamed Aziz Bhouri; Ritwik Gupta; Björn Lütjens; Justus C. Will; Gunnar Behrens; Julius Busecke; Nora Loose; Charles Stern; et al. (NeurIPS, 2023)