Pages that link to "Item:Q5023414"

The following pages link to Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations (Q5023414):

Displaying 50 items.

• An immersed boundary neural network for solving elliptic equations with singular forces on arbitrary domains (Q1980025) (← 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)
• Discretizationnet: a machine-learning based solver for Navier-Stokes equations using finite volume discretization (Q2021855) (← links)
• Non-invasive inference of thrombus material properties with physics-informed neural networks (Q2022055) (← links)
• Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks (Q2033658) (← links)
• PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
• Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (Q2072515) (← links)
• Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems (Q2072742) (← links)
• A representative volume element network (RVE-net) for accelerating RVE analysis, microscale material identification, and defect characterization (Q2072746) (← links)
• Interpretable machine learning: fundamental principles and 10 grand challenges (Q2074414) (← links)
• A physics-informed multi-fidelity approach for the estimation of differential equations parameters in low-data or large-noise regimes (Q2075654) (← links)
• Deep learning of conjugate mappings (Q2077602) (← links)
• Data-driven peakon and periodic peakon solutions and parameter discovery of some nonlinear dispersive equations via deep learning (Q2077801) (← links)
• Learning finite difference methods for reaction-diffusion type equations with FCNN (Q2079726) (← links)
• Data-driven discoveries of Bäcklund transformations and soliton evolution equations via deep neural network learning schemes (Q2081273) (← links)
• Monte Carlo fPINNs: deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations (Q2083146) (← links)
• Towards out of distribution generalization for problems in mechanics (Q2083180) (← links)
• Stabilized reduced-order models for unsteady incompressible flows in three-dimensional parametrized domains (Q2084084) (← links)
• Learning by neural networks under physical constraints for simulation in fluid mechanics (Q2101998) (← links)
• A Bayesian approach for data-driven dynamic equation discovery (Q2102994) (← links)
• Optimal control of PDEs using physics-informed neural networks (Q2106939) (← links)
• Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation (Q2106998) (← links)
• Multiresolution convolutional autoencoders (Q2112504) (← links)
• Prediction of optical solitons using an improved physics-informed neural network method with the conservation law constraint (Q2113135) (← links)
• Data-driven soliton solutions and model parameters of nonlinear wave models via the conservation-law constrained neural network method (Q2113241) (← links)
• A data-driven physics-informed finite-volume scheme for nonclassical undercompressive shocks (Q2124336) (← links)
• On the risks of using double precision in numerical simulations of spatio-temporal chaos (Q2124581) (← links)
• NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations (Q2127017) (← links)
• Deep learning of free boundary and Stefan problems (Q2128318) (← links)
• Learning non-Markovian physics from data (Q2128336) (← links)
• PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain (Q2128357) (← links)
• PFNN: a penalty-free neural network method for solving a class of second-order boundary-value problems on complex geometries (Q2128373) (← links)
• Image inversion and uncertainty quantification for constitutive laws of pattern formation (Q2131064) (← links)
• DeepM\&Mnet: inferring the electroconvection multiphysics fields based on operator approximation by neural networks (Q2131084) (← links)
• Using neural networks to accelerate the solution of the Boltzmann equation (Q2132591) (← links)
• A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media (Q2132604) (← links)
• Solving and learning nonlinear PDEs with Gaussian processes (Q2133484) (← links)
• Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation (Q2133495) (← 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)
• Physics-informed machine learning for reduced-order modeling of nonlinear problems (Q2133556) (← links)
• Simple computational strategies for more effective physics-informed neural networks modeling of turbulent natural convection (Q2133780) (← links)
• Data-driven discovery of multiscale chemical reactions governed by the law of mass action (Q2134528) (← links)
• DeLISA: deep learning based iteration scheme approximation for solving PDEs (Q2134800) (← links)
• A two-stage physics-informed neural network method based on conserved quantities and applications in localized wave solutions (Q2135816) (← links)
• When and why PINNs fail to train: a neural tangent kernel perspective (Q2136450) (← links)
• A general neural particle method for hydrodynamics modeling (Q2138776) (← links)
• Physics informed neural networks for continuum micromechanics (Q2138812) (← links)