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Machine Learning-Assisted Exploration of Thermally Conductive Polymers Based on High-Throughput Molecular Dynamics Simulations
Ruimin Ma, Hanfeng Zhang, Jiaxin Xu +4
Finding amorphous polymers with higher thermal conductivity is important, as they are ubiquitous in heat transfer applications. With recent progress in material informatics, machin…
Physics-informed Dyna-Style Model-Based Deep Reinforcement Learning for Dynamic Control
Xin-Yang Liu, Jian-Xun Wang
Model-based reinforcement learning (MBRL) is believed to have much higher sample efficiency compared to model-free algorithms by learning a predictive model of the environment. How…
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems
Han Gao, Matthew J. Zahr, Jian-Xun Wang
Despite the great promise of the physics-informed neural networks (PINNs) in solving forward and inverse problems, several technical challenges are present as roadblocks for more c…
Uncovering near-wall blood flow from sparse data with physics-informed neural networks
Amirhossein Arzani, Jian-Xun Wang, Roshan M. D'Souza
Near-wall blood flow and wall shear stress (WSS) regulate major forms of cardiovascular disease, yet they are challenging to quantify with high fidelity. Patient-specific computati…