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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 201–242 of 242 papers

TitleStatusHype
Beyond Monte Carlo: Harnessing Diffusion Models to Simulate Financial Market Dynamics—0
Bounds and Approximations for the Distribution of a Sum of Lognormal Random Variables—0
Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning models—0
Complete dimensional collapse in the continuum limit of a delayed SEIQR network model with separable distributed infectivity—0
Accurate and efficient Simulation of very high-dimensional Neural Mass Models with distributed-delay Connectome Tensors—0
Computing Modes of Instability of Parameterized Nonlinear Systems for Vulnerability Assessment—0
Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms—0
Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation—0
Constructing Low Star Discrepancy Point Sets with Genetic Algorithms—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
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps—0
Denoising Hamiltonian Network for Physical Reasoning—0
Differentiable Implicit Soft-Body Physics—0
Discretization of Linear Systems using the Matrix Exponential—0
Distance for Functional Data Clustering Based on Smoothing Parameter Commutation—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
Fast and accurate approximation of the full conditional for gamma shape parameters—0
Fast Approximate Bayesian Computation for Estimating Parameters in Differential Equations—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
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
Flow map matching with stochastic interpolants: A mathematical framework for consistency models—0
Flow Segmentation in Dense Crowds—0
Gaussian Processes and Reproducing Kernels: Connections and Equivalences—0
Gauss-Legendre Features for Gaussian Process Regression—0
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