Machine learning-assisted exploration of thermally conductive polymers based on high-throughput molecular dynamics simulations
Ruimin Ma(University of Notre Dame), Tengfei Luo(University of Notre Dame), Ryo Yoshida(The Institute of Statistical Mathematics), Junichiro Shiomi(The University of Tokyo), Yoshihiro Hayashi(The Institute of Statistical Mathematics), Jianxun Wang(Kunming University of Science and Technology), Jiaxin Xu(University of Notre Dame), Luning Sun(Nanjing University of Chinese Medicine), Hanfeng Zhang(University of Notre Dame)
Cited by 70
Related Papers
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
|Computer Methods in Applied Mechanics and Engineering|2019|972
PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
|Journal of Computational Physics|2020|577
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach
|Journal of Computational Physics|2016|304
Predictive large-eddy-simulation wall modeling via physics-informed neural networks
|Physical Review Fluids|2019|289
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems
|Computer Methods in Applied Mechanics and Engineering|2022|281