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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 51–100 of 242 papers

TitleStatusHype
Constructing Low Star Discrepancy Point Sets with Genetic Algorithms—0
Invariant Priors for Bayesian Quadrature—0
Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit—0
Continuous-time iterative linear-quadratic regulator—0
Convergence Guarantees for Adaptive Bayesian Quadrature Methods—0
Convergence guarantees for kernel-based quadrature rules in misspecified settings—0
Correlation Properties in Channels with von Mises-Fisher Distribution of Scatterers—0
Coupled Integral PINN for conservation law—0
D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory—0
Data-Augmented Numerical Integration in State Prediction: Rule Selection—0
Auto-Calibration and 2D-DOA Estimation in UCAs via an Integrated Wideband Dictionary—0
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps—0
A novel robust meta-analysis model using the t distribution for outlier accommodation and detection—0
Implicit Function Theorem: Estimates on the size of the domain—0
Lilan: A linear latent network approach for real-time solutions of stiff, nonlinear, ordinary differential equations—0
Beyond Monte Carlo: Harnessing Diffusion Models to Simulate Financial Market Dynamics—0
Implementation and (Inverse Modified) Error Analysis for implicitly-templated ODE-nets—0
Importance Sampling With Stochastic Particle Flow and Diffusion Optimization—0
Inverse Cubature and Quadrature Kalman filters—0
Adaptive quadrature schemes for Bayesian inference via active learning—0
A Neural ODE Interpretation of Transformer Layers—0
Graph Spring Neural ODEs for Link Sign Prediction—0
Efficient Modeling of Morphing Wing Flight Using Neural Networks and Cubature Rules—0
Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features—0
Domain-decoupled Physics-informed Neural Networks with Closed-form Gradients for Fast Model Learning of Dynamical Systems—0
Efficient Solution of Boolean Satisfiability Problems with Digital MemComputing—0
Bayesian Experimental Design for Symbolic Discovery—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
Bounds and Approximations for the Distribution of a Sum of Lognormal Random Variables—0
Exploration of methods for computing sensitivities in ODE models at dynamic and steady states—0
Extracting Dynamical Models from Data—0
Fast and accurate approximation of the full conditional for gamma shape parameters—0
Fast Approximate Bayesian Computation for Estimating Parameters in Differential Equations—0
An Equivalent Circuit Approach to Distributed Optimization—0
Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models—0
Fast Empirical Scenarios—0
Fast swaption pricing in Gaussian term structure models—0
A phenomenological spatial model for macro-ecological patterns in species-rich ecosystems—0
HEPGAME and the Simplification of Expressions—0
Least-Squares Neural Network (LSNN) Method For Scalar Nonlinear Hyperbolic Conservation Laws: Discrete Divergence Operator—0
Fitting mixed logit random regret minimization models using maximum simulated likelihood—0
Flow-based density of states for complex actions—0
A Closed-form Expression for the Gaussian Noise Model in the Presence of Raman Amplification—0
Flow Segmentation in Dense Crowds—0
Distance for Functional Data Clustering Based on Smoothing Parameter Commutation—0
A Bulirsch-Stoer algorithm using Gaussian processes—0
Gaussian Processes and Reproducing Kernels: Connections and Equivalences—0
Discretization of Linear Systems using the Matrix Exponential—0
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