Pages that link to "Item:Q2222972"
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The following pages link to Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks (Q2222972):
Displaying 50 items.
- Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics (Q2020980) (← links)
- A hybrid partitioned deep learning methodology for moving interface and fluid-structure interaction (Q2072360) (← links)
- PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
- Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (Q2072515) (← links)
- A Bayesian multiscale deep learning framework for flows in random media (Q2072635) (← links)
- Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems (Q2072742) (← links)
- Physics-constrained deep learning forecasting: an application with capacitance resistive model (Q2085076) (← links)
- Solving inverse problems using conditional invertible neural networks (Q2120777) (← links)
- Data-driven discovery of coarse-grained equations (Q2124010) (← links)
- PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain (Q2128357) (← links)
- On obtaining sparse semantic solutions for inverse problems, control, and neural network training (Q2132578) (← links)
- Extended dynamic mode decomposition for inhomogeneous problems (Q2132641) (← links)
- Learning time-dependent PDEs with a linear and nonlinear separate convolutional neural network (Q2135244) (← links)
- A neural network multigrid solver for the Navier-Stokes equations (Q2137963) (← links)
- CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method (Q2142144) (← links)
- Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data (Q2222275) (← links)
- Adversarial uncertainty quantification in physics-informed neural networks (Q2222278) (← links)
- Deep learning of dynamics and signal-noise decomposition with time-stepping constraints (Q2222431) (← links)
- A nonlocal physics-informed deep learning framework using the peridynamic differential operator (Q2237731) (← links)
- Deep autoencoders for physics-constrained data-driven nonlinear materials modeling (Q2237774) (← links)
- A physically constrained variational autoencoder for geochemical pattern recognition (Q2676496) (← links)
- Bayesian physics informed neural networks for real-world nonlinear dynamical systems (Q2679296) (← links)
- Long-time integration of parametric evolution equations with physics-informed DeepONets (Q2683074) (← links)
- Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning (Q2688065) (← links)
- An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator (Q2693426) (← 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)
- Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning (Q5162375) (← links)
- Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks (Q5221026) (← links)
- NeuralPDE: modelling dynamical systems from data (Q6041301) (← links)
- VI-DGP: a variational inference method with deep generative prior for solving high-dimensional inverse problems (Q6053024) (← links)
- PhySR: physics-informed deep super-resolution for spatiotemporal data (Q6054215) (← links)
- Physics-incorporated convolutional recurrent neural networks for source identification and forecasting of dynamical systems (Q6055145) (← links)
- Transformers for modeling physical systems (Q6055222) (← links)
- On the use of graph neural networks and shape‐function‐based gradient computation in the deep energy method (Q6092138) (← links)
- Adaptive learning of effective dynamics for online modeling of complex systems (Q6096436) (← links)
- On the use of neural networks for full waveform inversion (Q6096500) (← links)
- SeismicNET: physics-informed neural networks for seismic wave modeling in semi-infinite domain (Q6137634) (← links)
- Label-free learning of elliptic partial differential equation solvers with generalizability across boundary value problems (Q6146999) (← links)
- Identification of the flux function of nonlinear conservation laws with variable parameters (Q6156252) (← links)
- Bi-fidelity modeling of uncertain and partially unknown systems using DeepONets (Q6159313) (← links)
- Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations (Q6171723) (← links)
- Numerical Analysis for Convergence of a Sample-Wise Backpropagation Method for Training Stochastic Neural Networks (Q6190298) (← links)
- Physics-informed ConvNet: learning physical field from a shallow neural network (Q6199712) (← links)
- Bi-fidelity variational auto-encoder for uncertainty quantification (Q6202982) (← links)
- CEENs: causality-enforced evolutional networks for solving time-dependent partial differential equations (Q6557798) (← links)
- Graph network surrogate model for subsurface flow optimization (Q6560712) (← links)
- Advanced physics-informed neural networks for numerical approximation of the coupled Schrödinger-KdV equation (Q6590978) (← links)
- Pseudo grid-based physics-informed convolutional-recurrent network solving the integrable nonlinear lattice equations (Q6599876) (← links)
