Search Results - "Rao, Chengping"

Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization by Rao, Chengping, Liu, Yang

“…[Display omitted] •Proposed a three-dimensional convolutional neural network (3D-CNN) for heterogeneous composite material homogenization.•Network trained on…”
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Physics-Informed Deep Learning for Computational Elastodynamics without Labeled Data by Rao, Chengping, Sun, Hao, Liu, Yang

“…AbstractNumerical methods such as finite element have been flourishing in the past decades for modeling solid mechanics problems via solving governing partial…”
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Physics-informed deep learning for incompressible laminar flows by Rao, Chengping, Sun, Hao, Liu, Yang

“…•Proposed a mixed-variable physics-informed deep learning scheme for modeling incompressible laminar flows.•Used the general continuum and constitutive…”
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Numerical Simulation of the Solitary Wave Interacting with an Elastic Structure Using MPS-FEM Coupled Method by Rao, Chengping, Zhang, Youlin, Wan, Decheng

“…Fluid-Structure Interaction (FSI) caused by fluid impacting onto a flexible structure commonly occurs in naval architecture and ocean engineering. Research on…”
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PhyCRNet: Physics-informed convolutional-recurrent network for solving spatiotemporal PDEs by Ren, Pu, Rao, Chengping, Liu, Yang, Wang, Jian-Xun, Sun, Hao

“…Partial differential equations (PDEs) play a fundamental role in modeling and simulating problems across a wide range of disciplines. Recent advances in deep…”
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SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain by Ren, Pu, Rao, Chengping, Chen, Su, Wang, Jian-Xun, Sun, Hao, Liu, Yang

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Encoding physics to learn reaction–diffusion processes by Rao, Chengping, Ren, Pu, Wang, Qi, Buyukozturk, Oral, Sun, Hao, Liu, Yang

“…Modelling complex spatiotemporal dynamical systems, such as reaction–diffusion processes, which can be found in many fundamental dynamical effects in various…”
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PhySR: Physics-informed deep super-resolution for spatiotemporal data by Ren, Pu, Rao, Chengping, Liu, Yang, Ma, Zihan, Wang, Qi, Wang, Jian-Xun, Sun, Hao

“…High-fidelity simulation of complex physical systems is exorbitantly expensive and inaccessible across spatiotemporal scales. Recently, there has been an…”
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Baking Physics into Deep Learning for Modeling Scientific Problems by Rao, Chengping

“…In recent years, successful applications of deep learning (DL) have inspired scientists to explore the possibilities of applying DL approaches to modeling…”
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Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization by Rao, Chengping, Liu, Yang

“…Homogenization is a technique commonly used in multiscale computational science and engineering for predicting collective response of heterogeneous materials…”
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Physics-informed neural network for seismic wave inversion in layered semi-infinite domain by Ren, Pu, Rao, Chengping, Sun, Hao, Liu, Yang

“…Estimating the material distribution of Earth's subsurface is a challenging task in seismology and earthquake engineering. The recent development of…”
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Hard Encoding of Physics for Learning Spatiotemporal Dynamics by Rao, Chengping, Sun, Hao, Liu, Yang

“…Modeling nonlinear spatiotemporal dynamical systems has primarily relied on partial differential equations (PDEs). However, the explicit formulation of PDEs…”
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Physics informed deep learning for computational elastodynamics without labeled data by Rao, Chengping, Sun, Hao, Liu, Yang

“…Numerical methods such as finite element have been flourishing in the past decades for modeling solid mechanics problems via solving governing partial…”
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Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning by Rao, Chengping, Ren, Pu, Liu, Yang, Sun, Hao

“…There have been growing interests in leveraging experimental measurements to discover the underlying partial differential equations (PDEs) that govern complex…”
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Physics-informed deep learning for incompressible laminar flows by Rao, Chengping, Sun, Hao, Liu, Yang

“…Theoretical and Applied Mechanics Letters (2020) Physics-informed deep learning has drawn tremendous interest in recent years to solve computational physics…”
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PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs by Ren, Pu, Rao, Chengping, Liu, Yang, Wang, Jianxun, Sun, Hao

“…2022 Partial differential equations (PDEs) play a fundamental role in modeling and simulating problems across a wide range of disciplines. Recent advances in…”
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SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain by Ren, Pu, Rao, Chengping, Chen, Su, Wang, Jian-Xun, Sun, Hao, Liu, Yang

“…There has been an increasing interest in integrating physics knowledge and machine learning for modeling dynamical systems. However, very limited studies have…”
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Physics-informed Deep Super-resolution for Spatiotemporal Data by Ren, Pu, Rao, Chengping, Liu, Yang, Ma, Zihan, Wang, Qi, Wang, Jian-Xun, Sun, Hao

“…High-fidelity simulation of complex physical systems is exorbitantly expensive and inaccessible across spatiotemporal scales. Recently, there has been an…”
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Encoding physics to learn reaction-diffusion processes by Rao, Chengping, Ren, Pu, Wang, Qi, Buyukozturk, Oral, Sun, Hao, Liu, Yang

“…Modeling complex spatiotemporal dynamical systems, such as the reaction-diffusion processes, have largely relied on partial differential equations (PDEs)…”
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