Pages that link to "Item:Q5214836"

The following pages link to Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations (Q5214836):

Displaying 50 items.

• Overcoming the curse of dimensionality for some Hamilton-Jacobi partial differential equations via neural network architectures (Q783094) (← links)
• GANs for generating EFT models (Q823097) (← links)
• A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations (Q2072734) (← links)
• Solution of physics-based Bayesian inverse problems with deep generative priors (Q2083099) (← links)
• Efficient coupled deep neural networks for the time-dependent coupled Stokes-Darcy problems (Q2096255) (← links)
• Schwarz waveform relaxation-learning for advection-diffusion-reaction equations (Q2106899) (← links)
• B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data (Q2123977) (← links)
• Physics-informed semantic inpainting: application to geostatistical modeling (Q2125441) (← links)
• PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain (Q2128357) (← links)
• ISALT: inference-based schemes adaptive to large time-stepping for locally Lipschitz ergodic systems (Q2129142) (← links)
• Adversarial sampling of unknown and high-dimensional conditional distributions (Q2134718) (← links)
• Physics constrained learning for data-driven inverse modeling from sparse observations (Q2135255) (← links)
• Learning functional priors and posteriors from data and physics (Q2135824) (← links)
• Normalizing field flows: solving forward and inverse stochastic differential equations using physics-informed flow models (Q2138012) (← links)
• CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method (Q2142144) (← links)
• Learning generative neural networks with physics knowledge (Q2146912) (← links)
• Error estimates for deep learning methods in fluid dynamics (Q2149063) (← links)
• Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network (Q2157149) (← links)
• Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
• Data-driven rogue waves and parameters discovery in nearly integrable \(\mathcal{PT}\)-symmetric Gross-Pitaevskii equations via PINNs deep learning (Q2167994) (← links)
• Deep neural networks based temporal-difference methods for high-dimensional parabolic partial differential equations (Q2168314) (← links)
• Physics-informed neural networks for high-speed flows (Q2175317) (← links)
• Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data (Q2222275) (← links)
• Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems (Q2319398) (← links)
• A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations (Q2671335) (← links)
• Integrated finite element neural network (I-FENN) for non-local continuum damage mechanics (Q2678488) (← links)
• Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129) (← links)
• Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
• Neural network architectures using min-plus algebra for solving certain high-dimensional optimal control problems and Hamilton-Jacobi PDEs (Q2683498) (← links)
• Deep physics corrector: a physics enhanced deep learning architecture for solving stochastic differential equations (Q2687567) (← links)
• An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
• Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-informed Deep Generative Models (Q5022489) (← links)
• Generative Adversarial Network for Probabilistic Forecast of Random Dynamical Systems (Q5095487) (← links)
• Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow (Q5106295) (← links)
• DeepXDE: A Deep Learning Library for Solving Differential Equations (Q5150214) (← links)
• GAN-Based Priors for Quantifying Uncertainty in Supervised Learning (Q5158923) (← links)
• SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics (Q5161113) (← links)
• Bayesian differential programming for robust systems identification under uncertainty (Q5161165) (← links)
• Enforcing Imprecise Constraints on Generative Adversarial Networks for Emulating Physical Systems (Q5163887) (← links)
• Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks (Q5221026) (← links)
• Time-dependent Dirac equation with physics-informed neural networks: computation and properties (Q6043079) (← links)
• On the order of derivation in the training of physics-informed neural networks: case studies for non-uniform beam structures (Q6058580) (← links)
• Three ways to solve partial differential equations with neural networks — A review (Q6068232) (← links)
• Monte Carlo simulation of SDEs using GANs (Q6072362) (← links)
• Adaptive deep density approximation for fractional Fokker-Planck equations (Q6087826) (← links)
• Pre-training strategy for solving evolution equations based on physics-informed neural networks (Q6107095) (← links)
• Variable linear transformation improved physics-informed neural networks to solve thin-layer flow problems (Q6119307) (← links)
• Physics-guided training of GAN to improve accuracy in airfoil design synthesis (Q6121693) (← links)
• Pre-training physics-informed neural network with mixed sampling and its application in high-dimensional systems (Q6130985) (← links)
• A Normalizing Field Flow Induced Two-Stage Stochastic Homogenization Method for Random Composite Materials (Q6142999) (← links)