Pages that link to "Item:Q3296524"

The following pages link to Machine Learning for Fluid Mechanics (Q3296524):

Displaying 50 items.

• Machine learning strategies for systems with invariance properties (Q726815) (← links)
• GSA for machine learning problems: a comprehensive overview (Q823283) (← links)
• A data-driven smoothed particle hydrodynamics method for fluids (Q1980183) (← links)
• SciANN: a Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks (Q2020876) (← links)
• Development of an algorithm for reconstruction of droplet history based on deposition pattern using computational fluid dynamics and convolutional neural network (Q2021070) (← links)
• The neural particle method - an updated Lagrangian physics informed neural network for computational fluid dynamics (Q2021164) (← links)
• A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics (Q2021893) (← links)
• Diffusion maps-aided neural networks for the solution of parametrized PDEs (Q2021984) (← links)
• Embedding data analytics and CFD into the digital twin concept (Q2028120) (← links)
• Efficient computations for linear feedback control problems for target velocity matching of Navier-Stokes flows via POD and LSTM-ROM (Q2030428) (← links)
• PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
• Deep learning of conjugate mappings (Q2077602) (← links)
• Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression (Q2083714) (← links)
• Stabilized reduced-order models for unsteady incompressible flows in three-dimensional parametrized domains (Q2084084) (← links)
• Three-dimensional realizations of flood flow in large-scale rivers using the neural fuzzy-based machine-learning algorithms (Q2084088) (← links)
• Mapping of coherent structures in parameterized flows by learning optimal transportation with Gaussian models (Q2088388) (← links)
• Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response (Q2096901) (← links)
• Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation (Q2106998) (← links)
• Multiresolution convolutional autoencoders (Q2112504) (← links)
• DPM: a deep learning PDE augmentation method with application to large-eddy simulation (Q2123852) (← links)
• Deep learning of the spanwise-averaged Navier-Stokes equations (Q2123996) (← links)
• A data-driven physics-informed finite-volume scheme for nonclassical undercompressive shocks (Q2124336) (← links)
• Symplectic neural networks in Taylor series form for Hamiltonian systems (Q2124341) (← links)
• A long short-term memory embedding for hybrid uplifted reduced order models (Q2125587) (← links)
• NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations (Q2127017) (← links)
• Physically interpretable machine learning algorithm on multidimensional non-linear fields (Q2128352) (← links)
• PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain (Q2128357) (← links)
• Direct shape optimization through deep reinforcement learning (Q2128361) (← links)
• Physics-inspired architecture for neural network modeling of forces and torques in particle-laden flows (Q2129550) (← links)
• DeepM\&Mnet: inferring the electroconvection multiphysics fields based on operator approximation by neural networks (Q2131084) (← links)
• Deep reinforcement learning for the control of conjugate heat transfer (Q2131088) (← links)
• Data-driven fractional subgrid-scale modeling for scalar turbulence: a nonlocal LES approach (Q2133512) (← links)
• Mesh-Conv: convolution operator with mesh resolution independence for flow field modeling (Q2133572) (← links)
• Simple computational strategies for more effective physics-informed neural networks modeling of turbulent natural convection (Q2133780) (← links)
• Machine learning for fluid flow reconstruction from limited measurements (Q2134510) (← links)
• Data-driven discovery of multiscale chemical reactions governed by the law of mass action (Q2134528) (← links)
• Adversarial sampling of unknown and high-dimensional conditional distributions (Q2134718) (← links)
• A general neural particle method for hydrodynamics modeling (Q2138776) (← links)
• Stable \textit{a posteriori} LES of 2D turbulence using convolutional neural networks: backscattering analysis and generalization to higher \(Re\) via transfer learning (Q2139011) (← links)
• A robust super-resolution reconstruction model of turbulent flow data based on deep learning (Q2139577) (← links)
• Deep reinforcement learning of viscous incompressible flow (Q2162036) (← links)
• A simplified multilayer perceptron detector for the hybrid WENO scheme (Q2166567) (← links)
• A dynamic mode decomposition technique for the analysis of non-uniformly sampled flow data (Q2168306) (← links)
• Physics-informed distribution transformers via molecular dynamics and deep neural networks (Q2168329) (← links)
• Neural-network-based control of discrete-phase concentration in a gas-particle corner flow with optimal energy consumption (Q2194845) (← links)
• Turbulence theories and statistical closure approaches (Q2233981) (← links)
• Incorporating grain-scale processes in macroscopic sediment transport models. A review and perspectives for environmental and geophysical applications (Q2234258) (← links)
• Integration of neural networks with numerical solution of PDEs for closure models development (Q2234663) (← links)
• Recurrent neural networks (RNNs) learn the constitutive law of viscoelasticity (Q2241874) (← links)
• A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction (Q2245302) (← links)