Pages that link to "Item:Q3296524"

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

Displaying 50 items.

• Nonlinear mode decomposition with convolutional neural networks for fluid dynamics (Q5243565) (← links)
• Robust flow control and optimal sensor placement using deep reinforcement learning (Q5853740) (← links)
• Synchronization and chimeras in a network of four ring-coupled thermoacoustic oscillators (Q5863436) (← links)
• Reinforcement-learning-based control of convectively unstable flows (Q5870478) (← links)
• Reinforcement-learning-based actuator selection method for active flow control (Q5871612) (← links)
• A sparse optimal closure for a reduced-order model of wall-bounded turbulence (Q5876055) (← links)
• A transformer-based synthetic-inflow generator for spatially developing turbulent boundary layers (Q5878764) (← links)
• On the Generalizability of Machine-Learning-Assisted Anisotropy Mappings for Predictive Turbulence Modelling (Q5880411) (← links)
• Nonintrusive Reduced Order Modelling of Convective Boussinesq Flows (Q5880413) (← links)
• Numerical Assessment of a Nonintrusive Surrogate Model Based on Recurrent Neural Networks and Proper Orthogonal Decomposition: Rayleigh–Bénard Convection (Q5880415) (← links)
• Multi-Fidelity Machine Learning Applied to Steady Fluid Flows (Q5880416) (← links)
• Comparative analysis of machine learning methods for active flow control (Q5882038) (← links)
• Reconstruction of three-dimensional turbulent flow structures using surface measurements for free-surface flows based on a convolutional neural network (Q5884992) (← links)
• Reduced basis methods for time-dependent problems (Q5887836) (← links)
• \(\text{PIN}^{\mathcal L}\) : Preconditioned Inexact Newton with Learning Capability for Nonlinear System of Equations (Q6039247) (← links)
• Space-time adaptive model order reduction utilizing local low-dimensionality of flow field (Q6048451) (← links)
• Multiscale model reduction for incompressible flows (Q6052276) (← links)
• Markov chain generative adversarial neural networks for solving Bayesian inverse problems in physics applications (Q6052371) (← links)
• Deep learning-accelerated computational framework based on physics informed neural network for the solution of linear elasticity (Q6053463) (← links)
• Predicting turbulent dynamics with the convolutional autoencoder echo state network (Q6067855) (← links)
• Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution (Q6068233) (← links)
• A perspective on machine learning methods in turbulence modeling (Q6068270) (← links)
• Robust optimization and validation of echo state networks for learning chaotic dynamics (Q6079076) (← links)
• Adaptive deep density approximation for fractional Fokker-Planck equations (Q6087826) (← links)
• Symplectic learning for Hamiltonian neural networks (Q6087912) (← links)
• SONets: sub-operator learning enhanced neural networks for solving parametric partial differential equations (Q6087965) (← links)
• Active control of compressible channel flow up to Mab=3 using direct numerical simulations with spanwise velocity modulation at the walls (Q6089591) (← links)
• CD-ROM: complemented deep -- reduced order model (Q6094649) (← links)
• JAX-fluids: a fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows (Q6097327) (← links)
• FluxNet: a physics-informed learning-based Riemann solver for transcritical flows with non-ideal thermodynamics (Q6097610) (← links)
• Spiking recurrent neural networks for neuromorphic computing in nonlinear structural mechanics (Q6097653) (← links)
• Airfoil-based convolutional autoencoder and long short-term memory neural network for predicting coherent structures evolution around an airfoil (Q6100106) (← links)
• Deep learning closure models for large-eddy simulation of flows around bluff bodies (Q6114185) (← links)
• Neural-network-based fluid-structure interaction applied to vortex-induced vibration (Q6114712) (← links)
• \textit{FastSVD-ML-ROM}: a reduced-order modeling framework based on machine learning for real-time applications (Q6116133) (← links)
• Parsimony as the ultimate regularizer for physics-informed machine learning (Q6117148) (← links)
• A highly accurate strategy for data-driven turbulence modeling (Q6125393) (← links)
• Physics-based self-learning spiking neural network enhanced time-integration scheme for computing viscoplastic structural finite element response (Q6125507) (← links)
• A data-driven method for modelling dissipation rates in stratified turbulence (Q6144468) (← links)
• Four-dimensional variational data assimilation of a turbulent jet for super-temporal-resolution reconstruction (Q6145701) (← links)
• MC-Nonlocal-PINNs: Handling Nonlocal Operators in PINNs Via Monte Carlo Sampling (Q6151271) (← links)
• Data-driven wall modeling for turbulent separated flows (Q6158122) (← links)
• Machine learning for RANS turbulence modeling of variable property flows (Q6158535) (← links)
• Evaluation of physics constrained data-driven methods for turbulence model uncertainty quantification (Q6158540) (← links)
• On the improvement of the extrapolation capability of an iterative machine-learning based RANS framework (Q6158562) (← links)
• Blending machine learning and sequential data assimilation over latent spaces for surrogate modeling of Boussinesq systems (Q6160016) (← links)
• Data driven discovery of systems of ordinary differential equations using nonconvex multitask learning (Q6161211) (← links)
• Interactions among the rogue waves and solitons for a \((2 + 1)\)-dimensional Maccari system in fluid mechanics and nonlinear optics (Q6168218) (← links)
• Evolutionary sparse data-driven discovery of multibody system dynamics (Q6175006) (← links)
• Asymmetric secondary flows above geometrically symmetric surface roughness (Q6178510) (← links)