Pages that link to "Item:Q777521"

The following pages link to Deep learning observables in computational fluid dynamics (Q777521):

Displaying 50 items.

• Overcoming the curse of dimensionality for some Hamilton-Jacobi partial differential equations via neural network architectures (Q783094) (← links)
• Iterative surrogate model optimization (ISMO): an active learning algorithm for PDE constrained optimization with deep neural networks (Q2021252) (← links)
• Overcoming the curse of dimensionality in the numerical approximation of Allen-Cahn partial differential equations via truncated full-history recursive multilevel Picard approximations (Q2025321) (← links)
• Embedding data analytics and CFD into the digital twin concept (Q2028120) (← links)
• Constructive deep ReLU neural network approximation (Q2067309) (← links)
• On the approximation of rough functions with deep neural networks (Q2089012) (← links)
• Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs (Q2095545) (← links)
• Deep-learning accelerated calculation of real-fluid properties in numerical simulation of complex flowfields (Q2132659) (← links)
• DeepM\&Mnet for hypersonics: predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators (Q2133505) (← links)
• Deep neural network approximations for solutions of PDEs based on Monte Carlo algorithms (Q2152480) (← links)
• Overcoming the curse of dimensionality in the numerical approximation of parabolic partial differential equations with gradient-dependent nonlinearities (Q2162115) (← links)
• Physics-informed neural networks for high-speed flows (Q2175317) (← links)
• Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (Q2176917) (← links)
• Physics informed by deep learning: numerical solutions of modified Korteweg-de Vries equation (Q2244291) (← links)
• A review on deep reinforcement learning for fluid mechanics (Q2245392) (← links)
• Key time steps selection for CFD data based on deep metric learning (Q2334460) (← links)
• Integrated finite element neural network (I-FENN) for non-local continuum damage mechanics (Q2678488) (← links)
• Data-driven control of agent-based models: an equation/variable-free machine learning approach (Q2687520) (← links)
• Control of partial differential equations via physics-informed neural networks (Q2696946) (← links)
• An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
• Deep Splitting Method for Parabolic PDEs (Q4958922) (← links)
• Statistical solutions of hyperbolic systems of conservation laws: Numerical approximation (Q4960350) (← links)
• Enhancing Accuracy of Deep Learning Algorithms by Training with Low-Discrepancy Sequences (Q5001377) (← links)
• A multi-level procedure for enhancing accuracy of machine learning algorithms (Q5014840) (← links)
• Higher-Order Quasi-Monte Carlo Training of Deep Neural Networks (Q5015302) (← links)
• Generalized Cell Mapping Method with Deep Learning for Global Analysis and Response Prediction of Dynamical Systems (Q5016866) (← links)
• Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning (Q5019943) (← links)
• Learned turbulence modelling with differentiable fluid solvers: physics-based loss functions and optimisation horizons (Q5038552) (← links)
• Physics-Driven Learning of the Steady Navier-Stokes Equations using Deep Convolutional Neural Networks (Q5042008) (← links)
• (Q5053337) (← links)
• Machine learning active-nematic hydrodynamics (Q5073282) (← links)
• Approximations with deep neural networks in Sobolev time-space (Q5075578) (← links)
• Physics Informed Neural Networks (PINNs) For Approximating Nonlinear Dispersive PDEs (Q5079535) (← links)
• Deep Neural Network Surrogates for Nonsmooth Quantities of Interest in Shape Uncertainty Quantification (Q5097855) (← links)
• Numerical Simulations for Full History Recursive Multilevel Picard Approximations for Systems of High-Dimensional Partial Differential Equations (Q5162373) (← links)
• Statistical solutions of the incompressible Euler equations (Q5164208) (← links)
• Deep learning in turbulent convection networks (Q5218582) (← links)
• Data-driven prediction of unsteady flow over a circular cylinder using deep learning (Q5235730) (← links)
• Reynolds averaged turbulence modelling using deep neural networks with embedded invariance (Q5360504) (← links)
• Multi-Fidelity Machine Learning Applied to Steady Fluid Flows (Q5880416) (← links)
• On the approximation of functions by tanh neural networks (Q6055124) (← links)
• Three ways to solve partial differential equations with neural networks — A review (Q6068232) (← links)
• Continuous limits of residual neural networks in case of large input data (Q6098879) (← links)
• Neural vortex method: from finite Lagrangian particles to infinite dimensional Eulerian dynamics (Q6100103) (← links)
• Learning high frequency data via the coupled frequency predictor-corrector triangular DNN (Q6104304) (← links)
• NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators (Q6154538) (← links)
• A thermodynamics-informed active learning approach to perception and reasoning about fluids (Q6164293) (← links)
• Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing (Q6178392) (← links)
• On the spectral bias of coupled frequency predictor-corrector triangular DNN: the convergence analysis (Q6179933) (← links)
• Discontinuity computing using physics-informed neural networks (Q6184289) (← links)