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.

Most implemented papers

Learning Mesh-Based Simulation with Graph Networks

deepmind/deepmind-research ICLR 2021

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

afqueiruga/ContinuousNet 5 Aug 2020

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

zoj613/polya-gamma 2 May 2012

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

Quadrature-based features for kernel approximation

maremun/quffka ICLR 2018

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

Calibrating Multivariate Lévy Processes with Neural Networks

UnofficialJuliaMirror/ADCME.jl-07b341a0-ce75-57c6-b2de-414ffdc00be5 20 Dec 2018

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

torekleppe/PDPHMCpaperCode 4 May 2020

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.

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

goyalpike/RK4_SinDy 11 May 2021

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

felix.dietrich/sde-identification 10 Jun 2021

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

xxh0523/Py_PSNODE 25 Oct 2021

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.