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.

• Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems (Q2138842) (← links)
• CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method (Q2142144) (← links)
• Physics-informed neural networks for inverse problems in supersonic flows (Q2157127) (← links)
• Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
• Stochastic physics-informed neural ordinary differential equations (Q2168292) (← links)
• Predicting the dynamic process and model parameters of the vector optical solitons in birefringent fibers \textit{via} the modified PINN (Q2169695) (← links)
• Data-driven rogue waves and parameter discovery in the defocusing nonlinear Schrödinger equation with a potential using the PINN deep learning (Q2233120) (← links)
• Hidden physics model for parameter estimation of elastic wave equations (Q2236961) (← links)
• Prediction and identification of physical systems by means of physically-guided neural networks with meaningful internal layers (Q2236964) (← links)
• On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks (Q2237440) (← links)
• A nonlocal physics-informed deep learning framework using the peridynamic differential operator (Q2237731) (← links)
• TONR: an exploration for a novel way combining neural network with topology optimization (Q2246269) (← links)
• Data-driven vector soliton solutions of coupled nonlinear Schrödinger equation using a deep learning algorithm (Q2246919) (← links)
• A Bayesian framework to estimate fluid and material parameters in micro-swimmer models (Q2659810) (← links)
• High Reynolds number airfoil turbulence modeling method based on machine learning technique (Q2670056) (← links)
• Machine learning for vortex induced vibration in turbulent flow (Q2670071) (← links)
• Structure preservation for the deep neural network multigrid solver (Q2672194) (← links)
• Data-driven method to learn the most probable transition pathway and stochastic differential equation (Q2677788) (← links)
• Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes (Q2678495) (← links)
• A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks (Q2679440) (← links)
• Isogeometric analysis-based physics-informed graph neural network for studying traffic jam in neurons (Q2679502) (← links)
• Solving non-linear Kolmogorov equations in large dimensions by using deep learning: a numerical comparison of discretization schemes (Q2680327) (← links)
• Long-time integration of parametric evolution equations with physics-informed DeepONets (Q2683074) (← links)
• Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
• Physics-informed neural network methods based on Miura transformations and discovery of new localized wave solutions (Q2683577) (← links)
• Local parameter identification with neural ordinary differential equations (Q2690025) (← links)
• MFLP-PINN: a physics-informed neural network for multiaxial fatigue life prediction (Q2691055) (← links)
• A peridynamic-informed neural network for continuum elastic displacement characterization (Q2693390) (← links)
• Theoretical prerequisites for physically justified machine learning and its applications to fluid dynamics (Q2693664) (← links)
• Physics-informed neural networks based on adaptive weighted loss functions for Hamilton-Jacobi equations (Q2694112) (← links)
• Learning elliptic partial differential equations with randomized linear algebra (Q2697403) (← links)
• Hydrodynamic object recognition using pressure sensing (Q3090246) (← links)
• Machine Learning for Fluid Mechanics (Q3296524) (← links)
• Image-based blood flow estimation using a semi-analytical solution to the advection–diffusion equation in cylindrical domains (Q3382836) (← links)
• Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks (Q3389009) (← links)
• Accurate prediction of the particle image velocimetry flow field and rotor thrust using deep learning (Q3390379) (← links)
• Machine learning the kinematics of spherical particles in fluid flows (Q4559220) (← links)
• Deep learning of vortex-induced vibrations (Q4647380) (← links)
• Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks (Q4958918) (← links)
• Variational Inference Formulation for a Model-Free Simulation of a Dynamical System with Unknown Parameters by a Recurrent Neural Network (Q4986840) (← links)
• Data-driven resolvent analysis (Q4989070) (← links)
• A multi-level procedure for enhancing accuracy of machine learning algorithms (Q5014840) (← links)
• DIFFUSION ON FRACTAL OBJECTS MODELING AND ITS PHYSICS-INFORMED NEURAL NETWORK SOLUTION (Q5024806) (← links)
• PFNN-2: A Domain Decomposed Penalty-Free Neural Network Method for Solving Partial Differential Equations (Q5045670) (← links)
• MIONet: Learning Multiple-Input Operators via Tensor Product (Q5048574) (← links)
• VPVnet: A Velocity-Pressure-Vorticity Neural Network Method for the Stokes’ Equations under Reduced Regularity (Q5065192) (← links)
• Modern Koopman Theory for Dynamical Systems (Q5075835) (← links)
• Active Neuron Least Squares: A Training Method for Multivariate Rectified Neural Networks (Q5095494) (← links)
• (Q5104591) (← links)
• Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow (Q5106295) (← links)