Numerical Integration

27 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

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.

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.

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.

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 and power electronics, the need to build dynamic models of power system components based on accessible measurement data has become urgent.

Scalable Variational Inference for Dynamical Systems

ngorbach/Variational_Gradient_Matching_for_Dynamical_Systems NeurIPS 2017

That is why, despite the high computational cost, numerical integration is still the gold standard in many applications.

Batch Selection for Parallelisation of Bayesian Quadrature

OxfordML/bayesquad 4 Dec 2018

Integration over non-negative integrands is a central problem in machine learning (e. g. for model averaging, (hyper-)parameter marginalisation, and computing posterior predictive distributions).

AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs

gabb7/AReS-MaRS 22 Feb 2019

Stochastic differential equations are an important modeling class in many disciplines.