# Numerical Integration

50 papers with code • 0 benchmarks • 0 datasets

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

## Benchmarks

These leaderboards are used to track progress in Numerical Integration
## Most implemented papers

# Learning Mesh-Based Simulation with Graph Networks

Our model can be trained to pass messages on a mesh graph and to adapt the mesh discretization during forward simulation.

# Continuous-in-Depth Neural Networks

We first show that ResNets fail to be meaningful dynamical integrators in this richer sense.

# Bayesian inference for logistic models using Polya-Gamma latent variables

We propose a new data-augmentation strategy for fully Bayesian inference in models with binomial likelihoods.

# Quadrature-based features for kernel approximation

We consider the problem of improving kernel approximation via randomized feature maps.

# Calibrating Multivariate Lévy Processes with Neural Networks

Traditionally this problem can be solved with nonparametric estimation using the empirical characteristic functions (ECF), assuming certain regularity, and results to date are mostly in 1D.

# Connecting the Dots: Numerical Randomized Hamiltonian Monte Carlo with State-Dependent Event Rates

Numerical Generalized Randomized Hamiltonian Monte Carlo is introduced, as a robust, easy to use and computationally fast alternative to conventional Markov chain Monte Carlo methods for continuous target distributions.

# A note on the option price and 'Mass at zero in the uncorrelated SABR model and implied volatility asymptotics'

Gulisashvili et al. [Quant.

# Discovery of Nonlinear Dynamical Systems using a Runge-Kutta Inspired Dictionary-based Sparse Regression Approach

Discovering dynamical models to describe underlying dynamical behavior is essential to draw decisive conclusions and engineering studies, e. g., optimizing a process.

# Learning effective stochastic differential equations from microscopic simulations: linking stochastic numerics to deep learning

We identify effective stochastic differential equations (SDE) for coarse observables of fine-grained particle- or agent-based simulations; these SDE then provide useful coarse surrogate models of the fine scale dynamics.

# Feasibility Study of Neural ODE and DAE Modules for Power System Dynamic Component Modeling

In the context of high penetration of renewables, the need to build dynamic models of power system components based on accessible measurement data has become urgent.