Pages that link to "Item:Q5221026"

The following pages link to Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks (Q5221026):

Displaying 50 items.

• Overcoming the curse of dimensionality for some Hamilton-Jacobi partial differential equations via neural network architectures (Q783094) (← links)
• PPINN: parareal physics-informed neural network for time-dependent PDEs (Q2020276) (← links)
• A physics-informed operator regression framework for extracting data-driven continuum models (Q2020813) (← links)
• A multigrid multilevel Monte Carlo method for Stokes-Darcy model with random hydraulic conductivity and Beavers-Joseph condition (Q2067300) (← links)
• PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
• Data-driven peakon and periodic peakon solutions and parameter discovery of some nonlinear dispersive equations via deep learning (Q2077801) (← links)
• Data-driven discoveries of Bäcklund transformations and soliton evolution equations via deep neural network learning schemes (Q2081273) (← links)
• Optimal control of PDEs using physics-informed neural networks (Q2106939) (← links)
• A minimalistic approach to physics-informed machine learning using neighbour lists as physics-optimized convolutions for inverse problems involving particle systems (Q2106973) (← links)
• Learning stochastic dynamics with statistics-informed neural network (Q2112526) (← links)
• Data-driven deep learning of partial differential equations in modal space (Q2123370) (← links)
• On some neural network architectures that can represent viscosity solutions of certain high dimensional Hamilton-Jacobi partial differential equations (Q2123971) (← links)
• Structure-preserving neural networks (Q2127014) (← links)
• A stochastic collocation method based on sparse grids for a stochastic Stokes-Darcy model (Q2129156) (← links)
• Meta-mgnet: meta multigrid networks for solving parameterized partial differential equations (Q2133752) (← links)
• Thermodynamically consistent physics-informed neural networks for hyperbolic systems (Q2136443) (← links)
• When and why PINNs fail to train: a neural tangent kernel perspective (Q2136450) (← links)
• A neural network multigrid solver for the Navier-Stokes equations (Q2137963) (← links)
• A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data (Q2138799) (← links)
• Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems (Q2138842) (← links)
• Learning generative neural networks with physics knowledge (Q2146912) (← links)
• Mean-field and kinetic descriptions of neural differential equations (Q2148968) (← 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)
• HomPINNs: Homotopy physics-informed neural networks for learning multiple solutions of nonlinear elliptic differential equations (Q2172562) (← links)
• Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems (Q2222519) (← links)
• Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks (Q2223019) (← links)
• Data-driven vector soliton solutions of coupled nonlinear Schrödinger equation using a deep learning algorithm (Q2246919) (← links)
• A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations (Q2671335) (← links)
• A-WPINN algorithm for the data-driven vector-soliton solutions and parameter discovery of general coupled nonlinear equations (Q2677793) (← links)
• Data-driven spatiotemporal modeling for structural dynamics on irregular domains by stochastic dependency neural estimation (Q2678544) (← links)
• Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129) (← links)
• Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks (Q2683056) (← links)
• Neural network architectures using min-plus algebra for solving certain high-dimensional optimal control problems and Hamilton-Jacobi PDEs (Q2683498) (← links)
• An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
• Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning (Q5019943) (← links)
• An Augmented Lagrangian Deep Learning Method for Variational Problems with Essential Boundary Conditions (Q5065200) (← links)
• DeepXDE: A Deep Learning Library for Solving Differential Equations (Q5150214) (← links)
• Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations (Q5162369) (← links)
• On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs (Q5162370) (← links)
• Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions (Q5163229) (← links)
• Physics-Informed Neural Networks with Hard Constraints for Inverse Design (Q5165440) (← links)
• Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations (Q5214836) (← links)
• Randomized neural network with Petrov-Galerkin methods for solving linear and nonlinear partial differential equations (Q6058946) (← links)
• Application of the dynamical system method and the deep learning method to solve the new (3+1)-dimensional fractional modified Benjamin-Bona-Mahony equation (Q6059936) (← links)
• VC-PINN: variable coefficient physics-informed neural network for forward and inverse problems of PDEs with variable coefficient (Q6069931) (← links)
• Complex dynamics on the one-dimensional quantum droplets via time piecewise PINNs (Q6096531) (← links)
• Continuous limits of residual neural networks in case of large input data (Q6098879) (← links)
• Pre-training strategy for solving evolution equations based on physics-informed neural networks (Q6107095) (← links)
• Pseudo-Hamiltonian neural networks for learning partial differential equations (Q6119277) (← links)