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.

• Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities (Q2072477) (← links)
• PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (Q2072500) (← links)
• HiDeNN-TD: reduced-order hierarchical deep learning neural networks (Q2072507) (← links)
• Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (Q2072515) (← links)
• A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations (Q2072734) (← links)
• Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems (Q2072742) (← links)
• A representative volume element network (RVE-net) for accelerating RVE analysis, microscale material identification, and defect characterization (Q2072746) (← links)
• Interpretable machine learning: fundamental principles and 10 grand challenges (Q2074414) (← links)
• A physics-informed multi-fidelity approach for the estimation of differential equations parameters in low-data or large-noise regimes (Q2075654) (← links)
• At the crossroads of simulation and data analytics (Q2076004) (← links)
• Deep learning of conjugate mappings (Q2077602) (← links)
• Supervised learning from noisy observations: combining machine-learning techniques with data assimilation (Q2077682) (← links)
• Algorithms of data generation for deep learning and feedback design: a survey (Q2077720) (← links)
• Data-driven peakon and periodic peakon solutions and parameter discovery of some nonlinear dispersive equations via deep learning (Q2077801) (← links)
• Learning finite difference methods for reaction-diffusion type equations with FCNN (Q2079726) (← links)
• Data-driven discoveries of Bäcklund transformations and soliton evolution equations via deep neural network learning schemes (Q2081273) (← links)
• Estimating adsorption isotherm parameters in chromatography via a virtual injection promoting double feed-forward neural network (Q2082130) (← links)
• CENN: conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries (Q2083124) (← links)
• Monte Carlo fPINNs: deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations (Q2083146) (← links)
• Towards out of distribution generalization for problems in mechanics (Q2083180) (← links)
• NH-PINN: neural homogenization-based physics-informed neural network for multiscale problems (Q2083622) (← links)
• Neural-network based collision operators for the Boltzmann equation (Q2083624) (← links)
• Learning phase field mean curvature flows with neural networks (Q2083658) (← links)
• INN: interfaced neural networks as an accessible meshless approach for solving interface PDE problems (Q2083675) (← links)
• A physically-informed deep-learning model using time-reversal for locating a source from sparse and highly noisy sensors data (Q2083682) (← links)
• Data-driven solutions and parameter discovery of the Sasa-Satsuma equation via the physics-informed neural networks method (Q2083739) (← links)
• Stabilized reduced-order models for unsteady incompressible flows in three-dimensional parametrized domains (Q2084084) (← links)
• Solving PDEs by variational physics-informed neural networks: an a posteriori error analysis (Q2084593) (← links)
• Integrability and exact solutions of the (2+1)-dimensional KdV equation with Bell polynomials approach (Q2087663) (← links)
• Neural network-based parameter estimation of stochastic differential equations driven by Lévy noise (Q2088244) (← links)
• Error-correcting neural networks for semi-Lagrangian advection in the level-set method (Q2088344) (← links)
• Neural network approach to data-driven estimation of chemotactic sensitivity in the Keller-Segel model (Q2092227) (← links)
• The distortion of the Peregrine soliton under the perturbation in initial condition (Q2093720) (← links)
• Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs (Q2095545) (← links)
• Efficient coupled deep neural networks for the time-dependent coupled Stokes-Darcy problems (Q2096255) (← links)
• Stochastic modeling of inhomogeneities in the aortic wall and uncertainty quantification using a Bayesian encoder-decoder surrogate (Q2096832) (← links)
• A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method (Q2096848) (← links)
• Emulation of cardiac mechanics using graph neural networks (Q2096869) (← links)
• CAS4DL: Christoffel adaptive sampling for function approximation via deep learning (Q2098302) (← links)
• Lookback option pricing under the double Heston model using a deep learning algorithm (Q2099529) (← links)
• Committor functions via tensor networks (Q2099715) (← links)
• Multifidelity data fusion in convolutional encoder/decoder networks (Q2099723) (← links)
• A non-gradient method for solving elliptic partial differential equations with deep neural networks (Q2099748) (← links)
• Physics-informed neural networks for shell structures (Q2102673) (← links)
• A Bayesian approach for data-driven dynamic equation discovery (Q2102994) (← links)
• Physics-informed neural networks for gravity field modeling of small bodies (Q2104214) (← links)
• Solving partial differential equation based on extreme learning machine (Q2104376) (← links)
• Deep learning characterization of brain tumours with diffusion weighted imaging (Q2105512) (← links)
• Schwarz waveform relaxation-learning for advection-diffusion-reaction equations (Q2106899) (← links)
• Optimal control of PDEs using physics-informed neural networks (Q2106939) (← links)