Pages that link to "Item:Q2314336"

The following pages link to Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations (Q2314336):

Displaying 50 items.

• Weak adversarial networks for high-dimensional partial differential equations (Q777606) (← links)
• Stability analysis of hierarchical tensor methods for time-dependent PDEs (Q778308) (← links)
• Learning constitutive relations from indirect observations using deep neural networks (Q781968) (← links)
• Overcoming the curse of dimensionality for some Hamilton-Jacobi partial differential equations via neural network architectures (Q783094) (← links)
• Solving forward and inverse problems of the logarithmic nonlinear Schrödinger equation with \(\mathcal{PT}\)-symmetric harmonic potential via deep learning (Q822569) (← links)
• Non intrusive reduced order modeling of parametrized PDEs by kernel POD and neural networks (Q825483) (← links)
• Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space (Q825596) (← links)
• A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs (Q831238) (← links)
• An immersed boundary neural network for solving elliptic equations with singular forces on arbitrary domains (Q1980025) (← links)
• Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks (Q1989082) (← links)
• Deep learning acceleration of total Lagrangian explicit dynamics for soft tissue mechanics (Q1989089) (← links)
• PPINN: parareal physics-informed neural network for time-dependent PDEs (Q2020276) (← links)
• Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold (Q2020284) (← links)
• Efficient uncertainty quantification for dynamic subsurface flow with surrogate by theory-guided neural network (Q2020800) (← links)
• A physics-informed operator regression framework for extracting data-driven continuum models (Q2020813) (← links)
• SciANN: a Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks (Q2020876) (← links)
• Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics (Q2020980) (← links)
• Deep learned finite elements (Q2021024) (← links)
• A generic physics-informed neural network-based constitutive model for soft biological tissues (Q2021025) (← links)
• The neural particle method - an updated Lagrangian physics informed neural network for computational fluid dynamics (Q2021164) (← links)
• \textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition (Q2021230) (← links)
• Physics-informed machine learning models for predicting the progress of reactive-mixing (Q2021234) (← links)
• Iterative surrogate model optimization (ISMO): an active learning algorithm for PDE constrained optimization with deep neural networks (Q2021252) (← links)
• A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics (Q2021893) (← links)
• Deep learning of thermodynamics-aware reduced-order models from data (Q2021918) (← links)
• Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow (Q2021999) (← links)
• Non-invasive inference of thrombus material properties with physics-informed neural networks (Q2022055) (← links)
• Embedding data analytics and CFD into the digital twin concept (Q2028120) (← links)
• Spectral methods for nonlinear functionals and functional differential equations (Q2028689) (← links)
• Hierarchical deep-learning neural networks: finite elements and beyond (Q2033626) (← links)
• Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks (Q2033658) (← links)
• Continuous-time system identification with neural networks: model structures and fitting criteria (Q2034176) (← links)
• Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning (Q2035195) (← links)
• A deep learning improved numerical method for the simulation of rogue waves of nonlinear Schrödinger equation (Q2038155) (← links)
• A compressed lattice Boltzmann method based on ConvLSTM and resnet (Q2047572) (← links)
• Rank-adaptive tensor methods for high-dimensional nonlinear PDEs (Q2049083) (← links)
• Model reduction and neural networks for parametric PDEs (Q2050400) (← links)
• Sympnets: intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems (Q2057752) (← links)
• Machine learning for flux regression in discrete fracture networks (Q2059207) (← links)
• Gaussian process regression constrained by boundary value problems (Q2060077) (← links)
• POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition (Q2060079) (← links)
• Multi-fidelity meta modeling using composite neural network with online adaptive basis technique (Q2060166) (← links)
• PINN deep learning method for the Chen-Lee-Liu equation: rogue wave on the periodic background (Q2060632) (← links)
• Classification with Runge-Kutta networks and feature space augmentation (Q2063032) (← links)
• Urban planning image feature enhancement and simulation based on partial differential equation method (Q2064673) (← links)
• Objective-sensitive principal component analysis for high-dimensional inverse problems (Q2065835) (← links)
• Constructive deep ReLU neural network approximation (Q2067309) (← links)
• Estimating the time-dependent contact rate of SIR and SEIR models in mathematical epidemiology using physics-informed neural networks (Q2071475) (← links)
• A hybrid partitioned deep learning methodology for moving interface and fluid-structure interaction (Q2072360) (← links)
• Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks (Q2072449) (← links)