Pages that link to "Item:Q5068484"

The following pages link to Data-Driven Science and Engineering (Q5068484):

Displaying 33 items.

• Multiscale modeling of inelastic materials with thermodynamics-based artificial neural networks (TANN) (Q2160403) (← links)
• Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference (Q2678552) (← links)
• Machine Learning and Control Theory (Q5097114) (← links)
• Deep neural networks can stably solve high-dimensional, noisy, non-linear inverse problems (Q5873926) (← links)
• A case study of monkeypox disease in the United States using mathematical modeling with real data (Q6047623) (← links)
• A deep reinforcement learning framework for dynamic optimization of numerical schemes for compressible flow simulations (Q6048415) (← links)
• Epicasting: an ensemble wavelet neural network for forecasting epidemics (Q6057959) (← links)
• Data-Driven Discovery of Governing Equations for Coarse-Grained Heterogeneous Network Dynamics (Q6076413) (← links)
• Discovering stochastic partial differential equations from limited data using variational Bayes inference (Q6118584) (← links)
• Inverse parameter estimation using compressed sensing and POD-RBF reduced order models (Q6125474) (← links)
• Data-driven identification of parametric governing equations of dynamical systems using the signed cumulative distribution transform (Q6125481) (← links)
• Discovering efficient periodic behaviors in mechanical systems via neural approximators (Q6180273) (← links)
• Prediction of nonlocal elasticity parameters using high-throughput molecular dynamics simulations and machine learning (Q6181394) (← links)
• Energy-conserving hyper-reduction and temporal localization for reduced order models of the incompressible Navier-Stokes equations (Q6196594) (← links)
• A deep learning method for computing mean exit time excited by weak Gaussian noise (Q6539426) (← links)
• Efficient forecasting of chaotic systems with block-diagonal and binary reservoir computing (Q6548706) (← links)
• Dynamical and statistical properties of estimated high-dimensional ODE models: the case of the Lorenz '05 type II model (Q6549999) (← links)
• Data-driven robust iterative learning control of linear systems (Q6550244) (← links)
• Generalized quadratic embeddings for nonlinear dynamics using deep learning (Q6554923) (← links)
• Identification of network interactions from time series data: an iterative approach (Q6555020) (← links)
• Gradient preserving operator inference: data-driven reduced-order models for equations with gradient structure (Q6557793) (← links)
• An adaptive model order reduction technique for parameter-dependent modular structures (Q6558965) (← links)
• Using a library of chemical reactions to fit systems of ordinary differential equations to agent-based models: a machine learning approach (Q6559442) (← links)
• Dense outputs from quantum simulations (Q6589877) (← links)
• Effects of imperfections on the instability of a pipe conveying fluid: data-driven modeling and instability transition (Q6594340) (← links)
• Deep learning in computational mechanics: a review (Q6604128) (← links)
• Approximation of translation invariant Koopman operators for coupled non-linear systems (Q6604821) (← links)
• Dynamical pattern recognition for univariate time series and its application to an axial compressor (Q6630993) (← links)
• The discrete empirical interpolation method in class identification and data summarization (Q6642752) (← links)
• Physics-informed holomorphic neural networks (PIHNNs): solving 2D linear elasticity problems (Q6643566) (← links)
• A data-driven reduced-order modeling approach for parameterized time-domain Maxwell's equations (Q6647127) (← links)
• Neural dynamical operator: continuous spatial-temporal model with gradient-based and derivative-free optimization methods (Q6648386) (← links)
• Learning Hamiltonian dynamics with reproducing kernel Hilbert spaces and random features (Q6669750) (← links)