Pages that link to "Item:Q4644615"

The following pages link to Data-Driven Science and Engineering (Q4644615):

Displaying 50 items.

• Constraint-aware neural networks for Riemann problems (Q778316) (← links)
• Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering (Q2020738) (← links)
• Data-driven variational multiscale reduced order models (Q2020754) (← links)
• Physics-informed machine learning models for predicting the progress of reactive-mixing (Q2021234) (← links)
• Incentive rate determination in viral marketing (Q2029294) (← links)
• Data-driven dynamic interpolation and approximation (Q2059395) (← links)
• A ROM-accelerated parallel-in-time preconditioner for solving all-at-once systems in unsteady convection-diffusion PDEs (Q2060206) (← links)
• Objective-sensitive principal component analysis for high-dimensional inverse problems (Q2065835) (← links)
• An efficient iterative method for solving parameter-dependent and random convection-diffusion problems (Q2067305) (← links)
• Analysis and parametrical estimation with real COVID-19 data of a new extended SEIR epidemic model with quarantined individuals (Q2073579) (← links)
• Deep learning of conjugate mappings (Q2077602) (← links)
• On the motion of substance in a channel and growth of random networks (Q2078640) (← links)
• Multiresolution convolutional autoencoders (Q2112504) (← links)
• Data-driven modeling of linear dynamical systems with quadratic output in the AAA framework (Q2113660) (← links)
• Poincaré maps for multiscale physics discovery and nonlinear Floquet theory (Q2115543) (← links)
• Non-intrusive reduced-order modeling using uncertainty-aware deep neural networks and proper orthogonal decomposition: application to flood modeling (Q2123910) (← links)
• B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data (Q2123977) (← links)
• Data-driven modeling of two-dimensional detonation wave fronts (Q2124122) (← links)
• Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning (Q2127404) (← links)
• A comparative study of machine learning models for predicting the state of reactive mixing (Q2128488) (← links)
• Spatial early warning signals for tipping points using dynamic mode decomposition (Q2128710) (← links)
• A data-driven, physics-informed framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings (Q2129328) (← links)
• A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media (Q2132604) (← links)
• A Koopman framework for rare event simulation in stochastic differential equations (Q2133784) (← links)
• CFD-driven symbolic identification of algebraic Reynolds-stress models (Q2135797) (← links)
• Projection-tree reduced-order modeling for fast \(N\)-body computations (Q2137940) (← links)
• Unsteady physics-based reduced order modeling for large-scale compressible aerodynamic applications (Q2139580) (← links)
• Exploring time-delay-based numerical differentiation using principal component analysis (Q2139951) (← links)
• Learning biological dynamics from spatio-temporal data by Gaussian processes (Q2141319) (← links)
• Quantitative comparison of the mean-return-time phase and the stochastic asymptotic phase for noisy oscillators (Q2145423) (← links)
• An adaptive data-driven reduced order model based on higher order dynamic mode decomposition (Q2149050) (← links)
• Inadequacy of linear methods for minimal sensor placement and feature selection in nonlinear systems: a new approach using secants (Q2163754) (← links)
• Dissecting cell fate dynamics in pediatric glioblastoma through the lens of complex systems and cellular cybernetics (Q2165371) (← links)
• Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (Q2176917) (← links)
• Reduced order modeling of time-dependent incompressible Navier-Stokes equation with variable density based on a local radial basis functions-finite difference (LRBF-FD) technique and the POD/DEIM method (Q2180431) (← links)
• Sparse linear regression from perturbed data (Q2208606) (← links)
• On inhomogeneous nonholonomic Bilimovich system (Q2213529) (← links)
• A nonlocal physics-informed deep learning framework using the peridynamic differential operator (Q2237731) (← links)
• Adaptive mesh refinement and coarsening for diffusion-reaction epidemiological models (Q2241891) (← links)
• On the comparison of LES data-driven reduced order approaches for hydroacoustic analysis (Q2245201) (← links)
• Accelerating high order discontinuous Galerkin solvers using neural networks: 1D Burgers' equation (Q2670077) (← links)
• Coupled and uncoupled dynamic mode decomposition in multi-compartmental systems with applications to epidemiological and additive manufacturing problems (Q2670383) (← links)
• A non-intrusive method to inferring linear port-Hamiltonian realizations using time-domain data (Q2672195) (← links)
• On the Christoffel function and classification in data analysis (Q2673871) (← links)
• Efficient algorithm for proper orthogonal decomposition of block-structured adaptively refined numerical simulations (Q2675596) (← links)
• SVD perspectives for augmenting DeepONet flexibility and interpretability (Q2679470) (← links)
• Sparse dynamical system identification with simultaneous structural parameters and initial condition estimation (Q2680004) (← links)
• Locally-symplectic neural networks for learning volume-preserving dynamics (Q2681119) (← links)
• On the universal transformation of data-driven models to control systems (Q2681379) (← links)
• Extended dynamic mode decomposition for two paradigms of non-linear dynamical systems (Q2684647) (← links)