Numerical Integration
62 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.
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Most implemented papers
Learning Mesh-Based Simulation with Graph Networks
deepmind/deepmind-research
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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
We first show that ResNets fail to be meaningful dynamical integrators in this richer sense.
Outlier-Robust Neural Network Training: Efficient Optimization of Transformed Trimmed Loss with Variation Regularization
In this study, we consider outlier-robust predictive modeling using highly-expressive neural networks.
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
UnofficialJuliaMirror/ADCME.jl-07b341a0-ce75-57c6-b2de-414ffdc00be5
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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
felix.dietrich/sde-identification
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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.
