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Numerical Integration

Numerical integration is the task to calculate the numerical value of a definite integral or the numerical solution of differential equations.

Papers

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Showing 101–125 of 242 papers

TitleStatusHype
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond—0
Symbolic-Numeric Computation of Integrals in Successive Galerkin Approximation of Hamilton-Jacobi-Bellman Equation—0
Symplectic Gaussian Process Regression of Hamiltonian Flow Maps—0
Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration—0
Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence—0
A Renormalization Group Approach to Connect Discrete- and Continuous-Time Descriptions of Gaussian Processes—0
The mixed deep energy method for resolving concentration features in finite strain hyperelasticity—0
Time-Reversible Bridges of Data with Machine Learning—0
Time-vectorized numerical integration for systems of ODEs—0
Towards a Unified Quadrature Framework for Large-Scale Kernel Machines—0
Towards Hyperparameter-Agnostic DNN Training via Dynamical System Insights—0
Transfer learning enhanced physics informed neural network for phase-field modeling of fracture—0
Two-locus clines on the real line with a step environment—0
Understanding step selection analysis through numerical integration—0
What ODE-Approximation Schemes of Time-Delay Systems Reveal about Lyapunov-Krasovskii Functionals—0
Why Local Search Excels in Expression Simplification—0
Domain-decoupled Physics-informed Neural Networks with Closed-form Gradients for Fast Model Learning of Dynamical Systems—0
Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features—0
Efficient Modeling of Morphing Wing Flight Using Neural Networks and Cubature Rules—0
Efficient Solution of Boolean Satisfiability Problems with Digital MemComputing—0
EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems—0
Estimating and Assessing Differential Equation Models with Time-Course Data—0
Estimation of Spectral Risk Measures—0
Exploration of methods for computing sensitivities in ODE models at dynamic and steady states—0
Extracting Dynamical Models from Data—0
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