Pages that link to "Item:Q2176917"

The following pages link to Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (Q2176917):

Displaying 50 items.

• Improved deep neural networks with domain decomposition in solving partial differential equations (Q2674166) (← links)
• Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes (Q2678495) (← links)
• Data-driven spatiotemporal modeling for structural dynamics on irregular domains by stochastic dependency neural estimation (Q2678544) (← links)
• Isogeometric analysis-based physics-informed graph neural network for studying traffic jam in neurons (Q2679502) (← links)
• A metalearning approach for physics-informed neural networks (PINNs): application to parameterized PDEs (Q2681136) (← links)
• A physics-informed convolutional neural network for the simulation and prediction of two-phase Darcy flows in heterogeneous porous media (Q2681146) (← links)
• Isogeometric neural networks: a new deep learning approach for solving parameterized partial differential equations (Q2683423) (← links)
• Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
• Physics-integrated neural differentiable (PiNDiff) model for composites manufacturing (Q2686904) (← links)
• Solving free-surface problems for non-shallow water using boundary and initial conditions-free physics-informed neural network (bif-PINN) (Q2687566) (← links)
• Theoretical prerequisites for physically justified machine learning and its applications to fluid dynamics (Q2693664) (← links)
• Forecasting of nonlinear dynamics based on symbolic invariance (Q2701225) (← 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)
• 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)
• Physics-Driven Learning of the Steady Navier-Stokes Equations using Deep Convolutional Neural Networks (Q5042008) (← links)
• Microstructure-informed probability-driven point-particle model for hydrodynamic forces and torques in particle-laden flows (Q5116505) (← links)
• Multi-Fidelity Machine Learning Applied to Steady Fluid Flows (Q5880416) (← links)
• Physics-informed machine learning for surrogate modeling of wind pressure and optimization of pressure sensor placement (Q6044216) (← links)
• A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics (Q6044222) (← links)
• Multifidelity deep operator networks for data-driven and physics-informed problems (Q6048427) (← links)
• VI-DGP: a variational inference method with deep generative prior for solving high-dimensional inverse problems (Q6053024) (← links)
• Deep learning-accelerated computational framework based on physics informed neural network for the solution of linear elasticity (Q6053463) (← links)
• One-dimensional ice shelf hardness inversion: clustering behavior and collocation resampling in physics-informed neural networks (Q6054214) (← links)
• Dynamic analysis on optical pulses via modified PINNs: soliton solutions, rogue waves and parameter discovery of the CQ-NLSE (Q6058699) (← links)
• Deep learning phase‐field model for brittle fractures (Q6071412) (← links)
• Deep capsule encoder–decoder network for surrogate modeling and uncertainty quantification (Q6082494) (← links)
• Physics-informed data-driven model for fluid flow in porous media (Q6093463) (← links)
• Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems (Q6095075) (← links)
• Physics-informed deep learning for simultaneous surrogate modeling and PDE-constrained optimization of an airfoil geometry (Q6097587) (← links)
• Physics-informed neural networks with adaptive localized artificial viscosity (Q6107107) (← links)
• Predicting continuum breakdown with deep neural networks (Q6107123) (← links)
• Learning Markovian Homogenized Models in Viscoelasticity (Q6109142) (← links)
• Physics informed WNO (Q6120131) (← links)
• Solving seepage equation using physics-informed residual network without labeled data (Q6120152) (← links)
• Physics-based self-learning spiking neural network enhanced time-integration scheme for computing viscoplastic structural finite element response (Q6125507) (← links)
• SeismicNET: physics-informed neural networks for seismic wave modeling in semi-infinite domain (Q6137634) (← links)
• Variable separated physics-informed neural networks based on adaptive weighted loss functions for blood flow model (Q6144182) (← links)
• Label-free learning of elliptic partial differential equation solvers with generalizability across boundary value problems (Q6146999) (← links)
• NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators (Q6154538) (← links)
• On the improvement of the extrapolation capability of an iterative machine-learning based RANS framework (Q6158562) (← links)
• Embedding physical knowledge in deep neural networks for predicting the phonon dispersion curves of cellular metamaterials (Q6159334) (← links)
• An equivariant neural operator for developing nonlocal tensorial constitutive models (Q6162914) (← links)
• Automatic boundary fitting framework of boundary dependent physics-informed neural network solving partial differential equation with complex boundary conditions (Q6171169) (← links)
• Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations (Q6171723) (← links)
• Adaptive transfer learning for PINN (Q6173323) (← links)
• Hard enforcement of physics-informed neural network solutions of acoustic wave propagation (Q6180100) (← links)
• An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations (Q6191768) (← links)
• Spectral operator learning for parametric PDEs without data reliance (Q6194143) (← links)
• A complete physics-informed neural network-based framework for structural topology optimization (Q6194165) (← links)