Pages that link to "Item:Q2222287"

The following pages link to ConvPDE-UQ: convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains (Q2222287):

Displaying 29 items.

• ConvPDE-UQ (Q1333891) (← links)
• PPINN: parareal physics-informed neural network for time-dependent PDEs (Q2020276) (← links)
• PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
• A modified batch intrinsic plasticity method for pre-training the random coefficients of extreme learning machines (Q2133017) (← links)
• Learning time-dependent PDEs with a linear and nonlinear separate convolutional neural network (Q2135244) (← links)
• Output-based adaptive aerodynamic simulations using convolutional neural networks (Q2245362) (← links)
• Local extreme learning machines and domain decomposition for solving linear and nonlinear partial differential equations (Q2246361) (← links)
• On computing the hyperparameter of extreme learning machines: algorithm and application to computational PDEs, and comparison with classical and high-order finite elements (Q2671403) (← links)
• A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems (Q2672767) (← links)
• The Random Feature Model for Input-Output Maps between Banach Spaces (Q3382802) (← links)
• DeepXDE: A Deep Learning Library for Solving Differential Equations (Q5150214) (← links)
• Deep neural networks for waves assisted by the Wiener–Hopf method (Q5160881) (← links)
• Fully probabilistic deep models for forward and inverse problems in parametric PDEs (Q6095115) (← links)
• Accuracy and architecture studies of residual neural network method for ordinary differential equations (Q6101548) (← links)
• Addressing discontinuous root-finding for subsequent differentiability in machine learning, inverse problems, and control (Q6119249) (← links)
• Label-free learning of elliptic partial differential equation solvers with generalizability across boundary value problems (Q6146999) (← links)
• A deep neural network-based method for solving obstacle problems (Q6158276) (← links)
• Numerical computation of partial differential equations by hidden-layer concatenated extreme learning machine (Q6159015) (← links)
• Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets (Q6159313) (← links)
• Energy-dissipative evolutionary deep operator neural networks (Q6187616) (← links)
• Physics-informed ConvNet: learning physical field from a shallow neural network (Q6199712) (← links)
• On the locality of local neural operator in learning fluid dynamics (Q6557796) (← links)
• A causality-DeepONet for causal responses of linear dynamical systems (Q6584819) (← links)
• Operator learning using random features: a tool for scientific computing (Q6585281) (← links)
• Machine learning approaches for the solution of the Riemann problem in fluid dynamics: a case study (Q6593781) (← links)
• f-PICNN: a physics-informed convolutional neural network for partial differential equations with space-time domain (Q6614990) (← links)
• Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators (Q6663284) (← links)
• Convolutional autoencoders, clustering, and POD for low-dimensional parametrization of flow equations (Q6663360) (← links)
• From obstacle problems to neural insights: feedforward neural network modeling of ice thickness (Q6670343) (← links)