Numerical Integration
53 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
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
ma921/basq
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Empirically, we find that our approach significantly outperforms the sampling efficiency of both state-of-the-art BQ techniques and Nested Sampling in various real-world datasets, including lithium-ion battery analytics.
Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh Physics
CCSI-Toolset/MGN
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With this, we were able to train MGN on meshes with \textit{millions} of nodes to generate computational fluid dynamics (CFD) simulations.
A stochastic optimization approach to train non-linear neural networks with a higher-order variation regularization
While the $(k, q)$-VR terms applied to general parametric models are computationally intractable due to the integration, this study provides a stochastic optimization algorithm, that can efficiently train general models with the $(k, q)$-VR without conducting explicit numerical integration.
Scalable Variational Inference for Dynamical Systems
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
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
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Stochastic differential equations are an important modeling class in many disciplines.
Structured Variational Inference in Continuous Cox Process Models
VirgiAgl/STVB
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NeurIPS 2019
We propose a scalable framework for inference in an inhomogeneous Poisson process modeled by a continuous sigmoidal Cox process that assumes the corresponding intensity function is given by a Gaussian process (GP) prior transformed with a scaled logistic sigmoid function.
On two ways to use determinantal point processes for Monte Carlo integration
In the absence of DPP machinery to derive an efficient sampler and analyze their estimator, the idea of Monte Carlo integration with DPPs was stored in the cellar of numerical integration.
i-flow: High-dimensional Integration and Sampling with Normalizing Flows
i-flow/i-flow
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We introduce the code i-flow, a python package that performs high-dimensional numerical integration utilizing normalizing flows.
Hamiltonian neural networks for solving equations of motion
mariosmat/hamiltoniannnetodes
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There has been a wave of interest in applying machine learning to study dynamical systems.
