Pages that link to "Item:Q2222275"

The following pages link to Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data (Q2222275):

Displaying 50 items.

• A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder (Q2134764) (← links)
• Learning time-dependent PDEs with a linear and nonlinear separate convolutional neural network (Q2135244) (← links)
• Learning functional priors and posteriors from data and physics (Q2135824) (← links)
• Adaptive deep density approximation for Fokker-Planck equations (Q2135831) (← links)
• When and why PINNs fail to train: a neural tangent kernel perspective (Q2136450) (← links)
• Simulation of the 3D hyperelastic behavior of ventricular myocardium using a finite-element based neural-network approach (Q2136715) (← links)
• A sample-efficient deep learning method for multivariate uncertainty qualification of acoustic-vibration interaction problems (Q2138808) (← links)
• Learning finite element convergence with the multi-fidelity graph neural network (Q2145122) (← links)
• Learning generative neural networks with physics knowledge (Q2146912) (← links)
• Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network (Q2157149) (← links)
• Solving elliptic equations with Brownian motion: bias reduction and temporal difference learning (Q2157396) (← links)
• Probabilistic deep learning for real-time large deformation simulations (Q2160483) (← links)
• Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
• Physics-informed PointNet: a deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries (Q2168328) (← links)
• Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (Q2176917) (← links)
• Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square (Q2180429) (← links)
• Adversarial uncertainty quantification in physics-informed neural networks (Q2222278) (← links)
• A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the small data regime (Q2222510) (← links)
• Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems (Q2222519) (← links)
• A mesh-free method for interface problems using the deep learning approach (Q2222664) (← links)
• Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks (Q2222972) (← links)
• Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks (Q2223019) (← links)
• Dynamically orthogonal tensor methods for high-dimensional nonlinear PDEs (Q2223023) (← links)
• Coercing machine learning to output physically accurate results (Q2223280) (← links)
• Physics-informed multi-LSTM networks for metamodeling of nonlinear structures (Q2236167) (← links)
• Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets (Q2236928) (← links)
• Hidden physics model for parameter estimation of elastic wave equations (Q2236961) (← links)
• On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks (Q2237440) (← links)
• Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture (Q2237458) (← links)
• Theory-guided auto-encoder for surrogate construction and inverse modeling (Q2237777) (← links)
• A statistician teaches deep learning (Q2241468) (← links)
• Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian processes (Q2246340) (← links)
• Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems (Q2319398) (← links)
• Physics-informed Karhunen-Loéve and neural network approximations for solving inverse differential equation problems (Q2671323) (← links)
• Physics and equality constrained artificial neural networks: application to forward and inverse problems with multi-fidelity data fusion (Q2671417) (← links)
• DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method (Q2672762) (← links)
• A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems (Q2672767) (← links)
• Scalable uncertainty quantification for deep operator networks using randomized priors (Q2674111) (← links)
• The deep learning Galerkin method for the general Stokes equations (Q2674271) (← links)
• A physically constrained variational autoencoder for geochemical pattern recognition (Q2676496) (← links)
• DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations (Q2681099) (← links)
• A metalearning approach for physics-informed neural networks (PINNs): application to parameterized PDEs (Q2681136) (← links)
• Long-time integration of parametric evolution equations with physics-informed DeepONets (Q2683074) (← links)
• Isogeometric neural networks: a new deep learning approach for solving parameterized partial differential equations (Q2683423) (← links)
• Parameter estimation with the Markov chain Monte Carlo method aided by evolutionary neural networks in a water hammer model (Q2686514) (← links)
• opPINN: physics-informed neural network with operator learning to approximate solutions to the Fokker-Planck-Landau equation (Q2689626) (← links)
• An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
• Forecasting of nonlinear dynamics based on symbolic invariance (Q2701225) (← links)
• Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks (Q4958918) (← links)
• A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data Using Unstructured Spatial Discretizations (Q5005016) (← links)