Benchmarking of machine learning ocean subgrid parameterizations in an idealized model
Recently, a growing number of studies have used machine learning (ML) models to
parameterize computationally intensive subgrid‐scale processes in ocean models. Such …
parameterize computationally intensive subgrid‐scale processes in ocean models. Such …
Simulation of rarefied gas flows using physics-informed neural network combined with discrete velocity method
The linearized Bhatnagar–Gross–Krook equation is widely used to describe low-speed
rarefied gas flows and can be solved numerically using deterministic methods such as the …
rarefied gas flows and can be solved numerically using deterministic methods such as the …
Deep-Learning Strategy Based on Convolutional Neural Network for Wall Heat Flux Prediction
G Dai, W Zhao, S Yao, W Chen - AIAA Journal, 2023 - arc.aiaa.org
Aerodynamic thermal prediction plays an important role in the design of hypersonic aircraft,
especially in the design of the aircraft's thermal protection system. The main challenges of …
especially in the design of the aircraft's thermal protection system. The main challenges of …
Benchmarking of machine learning ocean subgrid parameterizations in an idealized model
Recently, a growing number of studies have used machine learning (ML) models to
parameterize computationally intensive subgrid-scale processes in ocean models. Such …
parameterize computationally intensive subgrid-scale processes in ocean models. Such …
Nonlinear constitutive calculation method of rarefied flow based on deep convolution neural networks
S Yao, W Zhao, C Wu, W Chen - Physics of Fluids, 2023 - pubs.aip.org
In the field of rarefied gas dynamics, the presence of non-equilibrium flow characteristics
poses significant challenges for achieving efficient and accurate numerical simulation …
poses significant challenges for achieving efficient and accurate numerical simulation …
An invariance constrained deep learning network for partial differential equation discovery
C Chen, H Li, X Jin - Physics of Fluids, 2024 - pubs.aip.org
The discovery of partial differential equations (PDEs) from datasets has attracted increased
attention. However, the discovery of governing equations from sparse data with high noise is …
attention. However, the discovery of governing equations from sparse data with high noise is …
Dimensional homogeneity constrained gene expression programming for discovering governing equations
Data-driven discovery of governing equations is of great significance for helping us
understand intrinsic mechanisms and build physical models. Recently, numerous highly …
understand intrinsic mechanisms and build physical models. Recently, numerous highly …
Dimensional homogeneity constrained gene expression programming for discovering governing equations from noisy and scarce data
Data-driven discovery of governing equations is of great significance for helping us
understand intrinsic mechanisms and build physical models. However, it is still not trivial for …
understand intrinsic mechanisms and build physical models. However, it is still not trivial for …
An invariance constrained deep learning network for PDE discovery
C Chen, H Li, X Jin - arXiv preprint arXiv:2402.03747, 2024 - arxiv.org
The discovery of partial differential equations (PDEs) from datasets has attracted increased
attention. However, the discovery of governing equations from sparse data with high noise is …
attention. However, the discovery of governing equations from sparse data with high noise is …
Data-driven discovery of the governing equation of granular flow in the homogeneous cooling state using sparse regression
With the arrival of the era of big data and the rapid development of high-precision discrete
simulations, a wealth of high-quality data is readily available, but discovering physical laws …
simulations, a wealth of high-quality data is readily available, but discovering physical laws …