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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 126–150 of 242 papers

TitleStatusHype
Implicit Function Theorem: Estimates on the size of the domain—0
Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNsCode0
Flow-based density of states for complex actions—0
What ODE-Approximation Schemes of Time-Delay Systems Reveal about Lyapunov-Krasovskii Functionals—0
Splitting numerical integration for matrix completion—0
Fenrir: Physics-Enhanced Regression for Initial Value ProblemsCode0
Inertial Navigation Using an Inertial Sensor ArrayCode0
Mars Entry Trajectory Planning with Range Discretization and Successive Convexification—0
Small-Signal Stability Analysis of Numerical Integration Methods—0
Learned Cone-Beam CT Reconstruction Using Neural Ordinary Differential Equations—0
Machine learning prediction for mean motion resonance behaviour -- The planar case—0
Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEsCode0
`Next Generation' Reservoir Computing: an Empirical Data-Driven Expression of Dynamical Equations in Time-Stepping Form—0
Invariant Priors for Bayesian Quadrature—0
A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel—0
Small-Signal Stability Techniques for Power System Modal Analysis, Control, and Numerical Integration—0
Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing—0
Least-Squares Neural Network (LSNN) Method For Scalar Nonlinear Hyperbolic Conservation Laws: Discrete Divergence Operator—0
Extracting Dynamical Models from Data—0
Efficient Modeling of Morphing Wing Flight Using Neural Networks and Cubature Rules—0
Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit—0
Learning Dynamics from Noisy Measurements using Deep Learning with a Runge-Kutta Constraint—0
Data-based stochastic modeling reveals sources of activity bursts in single-cell TGF-β signalingCode0
Revisiting the Effects of Stochasticity for Hamiltonian Samplers—0
Distributional Gradient Matching for Learning Uncertain Neural Dynamics ModelsCode0
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