Numerical Integration

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Most implemented papers archive 2025-07-28

30 shown of 70 papers with code (242 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Learning Mesh-Based Simulation with Graph Networks
  7 Oct 2020 11 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 1 pointer-only (licence)
  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
  5 Aug 2020 4 repositories listed Syntology ran 4 of 6 samples · 2 unverified · 6 pointer-only (licence)
  We first show that ResNets fail to be meaningful dynamical integrators in this richer sense.
• Outlier-robust neural network training: variation regularization meets trimmed loss to prevent functional breakdown
  4 Aug 2023 2 repositories listed
  In this study, we tackle the challenge of outlier-robust predictive modeling using highly expressive neural networks.
• Minimizing robust density power-based divergences for general parametric density models
  11 Jul 2023 2 repositories listed
  Density power divergence (DPD) is designed to robustly estimate the underlying distribution of observations, in the presence of outliers.
• Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh Physics
  1 Apr 2023 2 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  With this, we were able to train MGN on meshes with \textit{millions} of nodes to generate computational fluid dynamics (CFD) simulations.
• Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
  9 Jun 2022 2 repositories listed Syntology ran 1 of 8 samples · 7 unverified
  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.
• Feasibility Study of Neural ODE and DAE Modules for Power System Dynamic Component Modeling
  25 Oct 2021 2 repositories listed
  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.
• Learning effective stochastic differential equations from microscopic simulations: linking stochastic numerics to deep learning
  10 Jun 2021 2 repositories listed
  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.
• Discovery of Nonlinear Dynamical Systems using a Runge-Kutta Inspired Dictionary-based Sparse Regression Approach
  11 May 2021 2 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  Discovering dynamical models to describe underlying dynamical behavior is essential to draw decisive conclusions and engineering studies, e.
• A note on the option price and 'Mass at zero in the uncorrelated SABR model and implied volatility asymptotics'
  1 Nov 2020 2 repositories listed
  Gulisashvili et al.
• Connecting the Dots: Numerical Randomized Hamiltonian Monte Carlo with State-Dependent Event Rates
  4 May 2020 2 repositories listed
  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.
• Calibrating Multivariate Lévy Processes with Neural Networks
  20 Dec 2018 2 repositories listed
  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.
• Quadrature-based features for kernel approximation
  11 Feb 2018 2 repositories listed
  We consider the problem of improving kernel approximation via randomized feature maps.
• Bayesian inference for logistic models using Polya-Gamma latent variables
  2 May 2012 2 repositories listed
  We propose a new data-augmentation strategy for fully Bayesian inference in models with binomial likelihoods.
• How Far Are We from Optimal Reasoning Efficiency?
  8 Jun 2025 1 repository listed
  To reduce the efficiency gap, we propose REO-RL, a class of Reinforcement Learning algorithms that minimizes REG by targeting a sparse set of token budgets.
• A Dictionary of Closed-Form Kernel Mean Embeddings
  26 Apr 2025 1 repository listed
  Kernel mean embeddings -- integrals of a kernel with respect to a probability distribution -- are essential in Bayesian quadrature, but also widely used in other computational tools for numerical integration or for…
• Force-Free Molecular Dynamics Through Autoregressive Equivariant Networks
  31 Mar 2025 1 repository listed
  Molecular dynamics (MD) simulations play a crucial role in scientific research.
• Underdamped Diffusion Bridges with Applications to Sampling
  2 Mar 2025 1 repository listed Syntology ran 0 of 10 samples · 10 unverified
  We provide a general framework for learning diffusion bridges that transport prior to target distributions.
• Provable Quantum Algorithm Advantage for Gaussian Process Quadrature
  20 Feb 2025 1 repository listed
  In this paper, we combine Gaussian process quadratures and quantum computing by proposing a quantum low-rank Gaussian process quadrature method based on a Hilbert space approximation of the Gaussian process kernel and…
• FNIN: A Fourier Neural Operator-based Numerical Integration Network for Surface-form-gradients
  21 Jan 2025 1 repository listed
  Traditional methods encounter significant challenges in achieving high accuracy and handling high-resolution inputs, particularly facing the complex nature of discontinuities and the inefficiencies associated with…
• Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates
  17 Dec 2024 1 repository listed Syntology ran 0 of 8 samples · 8 unverified · 8 pointer-only (licence)
  Neural surrogates for partial differential equations (PDEs) have become popular due to their potential to quickly simulate physics.
• Adaptive Process-Guided Learning: An Application in Predicting Lake DO Concentrations
  20 Nov 2024 1 repository listed
  Unlike traditional RNNs, which may deliver high accuracy but often lack physical consistency and broad applicability, the \textit{Pril} method incorporates differential DO equations for each lake layer, modeling it as a…
• GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systems
  6 Oct 2024 1 repository listed
  This study presents GreenLight-Gym, a new, fast, open-source benchmark environment for developing reinforcement learning (RL) methods in greenhouse crop production control.
• Average Causal Effect Estimation in DAGs with Hidden Variables: Extensions of Back-Door and Front-Door Criteria
  6 Sep 2024 1 repository listed
  The identification theory for causal effects in directed acyclic graphs (DAGs) with hidden variables is well-developed, but methods for estimating and inferring functionals beyond the g-formula remain limited.
• Physics-informed nonlinear vector autoregressive models for the prediction of dynamical systems
  25 Jul 2024 1 repository listed
  Machine learning techniques have recently been of great interest for solving differential equations.
• Improving the Training of Rectified Flows
  30 May 2024 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  In this work, we propose improved techniques for training rectified flows, allowing them to compete with \emph{knowledge distillation} methods even in the low NFE setting.
• Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks
  23 May 2024 1 repository listed Syntology ran 2 of 2 samples · 0 unverified
  In fact, MPMC points are empirically shown to be either optimal or near-optimal with respect to the discrepancy for low dimension and small number of points, i.
• Identifiability of Differential-Algebraic Systems
  22 May 2024 1 repository listed
  A fundamental requirement is the existence of a unique set of parameters for a chosen model structure, an issue commonly referred to as identifiability.
• Evaluating AI-generated code for C++, Fortran, Go, Java, Julia, Matlab, Python, R, and Rust
  21 May 2024 1 repository listed
  To this end, we asked ChatGPT to generate three distinct codes: a simple numerical integration, a conjugate gradient solver, and a parallel 1D stencil-based heat equation solver.
• Combining Normalizing Flows and Quasi-Monte Carlo
  11 Jan 2024 1 repository listed
  Recent advances in machine learning have led to the development of new methods for enhancing Monte Carlo methods such as Markov chain Monte Carlo (MCMC) and importance sampling (IS).

Syntology lines on 9 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.