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Computational Fluid Mechanics And Heat Transfer
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Hypersonic and high temperature gas dynamics
TL;DR: In this article, the authors discuss the properties of high-temperature gas dynamics, including the effects of high temperature on the dynamics of Viscous Flow and Vibrational Nonequilibrium Flows.
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PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
Han Gao,Luning Sun,Jian-Xun Wang +2 more
TL;DR: A novel physics-constrained CNN learning architecture, aiming to learn solutions of parametric PDEs on irregular domains without any labeled data is proposed, and elliptic coordinate mapping is introduced to enable coordinate transforms between the irregular physical domain and regular reference domain.
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Numerical modeling of subaerial and submarine landslide-generated tsunami waves—recent advances and future challenges
TL;DR: A review of numerical studies on landslides can be found in this article, where the main landslide events followed by an LGW hazard are scrutinized and the remaining challenges are reviewed as the necessity of probabilistic analysis to assess the risk of the related hazards more accurately.
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Distributed optimal control for multi-agent trajectory optimization
TL;DR: This paper presents a novel optimal control problem that is applicable to multiscale dynamical systems comprised of numerous interacting agents that is derived analytically and demonstrated numerically through a multi-agent trajectory optimization problem.
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Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
Han Gao,Luning Sun,Jian-Xun Wang +2 more
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References
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Book
Hypersonic and high temperature gas dynamics
TL;DR: In this article, the authors discuss the properties of high-temperature gas dynamics, including the effects of high temperature on the dynamics of Viscous Flow and Vibrational Nonequilibrium Flows.
Journal ArticleDOI
PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
Han Gao,Luning Sun,Jian-Xun Wang +2 more
TL;DR: A novel physics-constrained CNN learning architecture, aiming to learn solutions of parametric PDEs on irregular domains without any labeled data is proposed, and elliptic coordinate mapping is introduced to enable coordinate transforms between the irregular physical domain and regular reference domain.
Journal ArticleDOI
Numerical modeling of subaerial and submarine landslide-generated tsunami waves—recent advances and future challenges
TL;DR: A review of numerical studies on landslides can be found in this article, where the main landslide events followed by an LGW hazard are scrutinized and the remaining challenges are reviewed as the necessity of probabilistic analysis to assess the risk of the related hazards more accurately.
Journal ArticleDOI
Distributed optimal control for multi-agent trajectory optimization
TL;DR: This paper presents a novel optimal control problem that is applicable to multiscale dynamical systems comprised of numerous interacting agents that is derived analytically and demonstrated numerically through a multi-agent trajectory optimization problem.
Journal ArticleDOI
Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
Han Gao,Luning Sun,Jian-Xun Wang +2 more
TL;DR: This work presents a novel physics-informed DL-based SR solution using convolutional neural networks (CNN), which is able to produce HR flow fields from low-resolution (LR) inputs in high-dimensional parameter space by leveraging the conservation laws and boundary conditions of fluid flows.