Pages that link to "Item:Q1685467"

The following pages link to Inferring solutions of differential equations using noisy multi-fidelity data (Q1685467):

Displaying 50 items.

• Gamblets for opening the complexity-bottleneck of implicit schemes for hyperbolic and parabolic ODEs/PDEs with rough coefficients (Q683388) (← links)
• Overcoming the curse of dimensionality for some Hamilton-Jacobi partial differential equations via neural network architectures (Q783094) (← links)
• A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs (Q831238) (← links)
• Machine learning of linear differential equations using Gaussian processes (Q1694641) (← links)
• Discovering variable fractional orders of advection-dispersion equations from field data using multi-fidelity Bayesian optimization (Q1694642) (← links)
• Hidden physics models: machine learning of nonlinear partial differential equations (Q1699464) (← links)
• Model-free data-driven inelasticity (Q1987961) (← links)
• IDENT: identifying differential equations with numerical time evolution (Q1995989) (← links)
• Hierarchical deep-learning neural networks: finite elements and beyond (Q2033626) (← links)
• Numerical solution of the parametric diffusion equation by deep neural networks (Q2049099) (← links)
• Image segmentation method for an illumination highlight region of interior design effects based on the partial differential equation (Q2051962) (← links)
• Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities (Q2072477) (← links)
• A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations (Q2072734) (← links)
• A physics-informed multi-fidelity approach for the estimation of differential equations parameters in low-data or large-noise regimes (Q2075654) (← links)
• Deep learning of conjugate mappings (Q2077602) (← links)
• A generalized probabilistic learning approach for multi-fidelity uncertainty quantification in complex physical simulations (Q2083198) (← links)
• Physics-constrained deep learning forecasting: an application with capacitance resistive model (Q2085076) (← links)
• An application of the splitting-up method for the computation of a neural network representation for the solution for the filtering equations (Q2093308) (← links)
• Learning by neural networks under physical constraints for simulation in fluid mechanics (Q2101998) (← links)
• A physics-informed learning approach to Bernoulli-type free boundary problems (Q2107176) (← links)
• Bridging the gap: machine learning to resolve improperly modeled dynamics (Q2116291) (← links)
• Feasibility of DEIM for retrieving the initial field via dimensionality reduction (Q2120024) (← links)
• Bayesian optimization with output-weighted optimal sampling (Q2123965) (← links)
• B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data (Q2123977) (← links)
• A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables (Q2124009) (← links)
• Symplectic neural networks in Taylor series form for Hamiltonian systems (Q2124341) (← links)
• Multi-fidelity Bayesian neural networks: algorithms and applications (Q2124403) (← links)
• Active training of physics-informed neural networks to aggregate and interpolate parametric solutions to the Navier-Stokes equations (Q2124408) (← links)
• Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models (Q2124564) (← links)
• Deep neural networks and adaptive quadrature for solving variational problems (Q2128466) (← links)
• A robust framework for identification of PDEs from noisy data (Q2133542) (← links)
• Enforcing exact physics in scientific machine learning: a data-driven exterior calculus on graphs (Q2133772) (← links)
• Learning finite element convergence with the multi-fidelity graph neural network (Q2145122) (← links)
• Numerical bifurcation analysis of PDEs from lattice Boltzmann model simulations: a parsimonious machine learning approach (Q2149520) (← links)
• Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
• A data-driven surrogate to image-based flow simulations in porous media (Q2176870) (← links)
• A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems (Q2180467) (← links)
• Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems (Q2184334) (← links)
• Enforcing boundary conditions on physical fields in Bayesian inversion (Q2186856) (← links)
• Assessment of end-to-end and sequential data-driven learning for non-intrusive modeling of fluid flows (Q2190672) (← links)
• Methods to recover unknown processes in partial differential equations using data (Q2210652) (← links)
• Sharp interface approaches and deep learning techniques for multiphase flows (Q2214553) (← links)
• Optimal observations-based retrieval of topography in 2D shallow water equations using PC-EnKF (Q2214574) (← links)
• Neural-net-induced Gaussian process regression for function approximation and PDE solution (Q2214653) (← 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 physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the small data regime (Q2222510) (← links)
• A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems (Q2222703) (← links)
• Adaptive activation functions accelerate convergence in deep and physics-informed neural networks (Q2223034) (← links)
• Deep multiscale model learning (Q2223279) (← links)