Papers with Code 아카이브

Numerical Integration

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Numerical integration is the task to calculate the numerical value of a definite integral or the numerical solution of differential equations.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/maremun/quffka

공식 코드

Quadrature-based features for kernel approximation

https://github.com/mariosmat/hamiltoniannnetodes

공식 코드

Hamiltonian neural networks for solving equations of motion

https://github.com/mmahsereci/kernel_embedding_dictionary

공식 코드

A Dictionary of Closed-Form Kernel Mean Embeddings

https://github.com/montanariarthur/identifiabilitydae

공식 코드

Identifiability of Differential-Algebraic Systems

https://github.com/n-gao/pesnet

공식 코드

Sampling-free Inference for Ab-Initio Potential Energy Surface Networks

https://github.com/nailwatts/fnin

공식 코드

FNIN: A Fourier Neural Operator-based Numerical Integration Network for Surface-form-gradients

https://github.com/nathanaelbosch/fenrir-experiments

공식 코드

Fenrir: Physics-Enhanced Regression for Initial Value Problems

https://github.com/nklb/stochastic-smad

공식 코드

Data-based stochastic modeling reveals sources of activity bursts in single-cell TGF-$β$ signaling

https://github.com/oknakfm/dpd

공식 코드

Minimizing robust density power-based divergences for general parametric density models

https://github.com/oknakfm/hotv

공식 코드

Outlier-robust neural network training: variation regularization meets trimmed loss to prevent functional breakdown

https://github.com/oknakfm/hovr

공식 코드

Outlier-robust neural network training: variation regularization meets trimmed loss to prevent functional breakdown

https://github.com/oknakfm/sgdpd

공식 코드

Minimizing robust density power-based divergences for general parametric density models

https://github.com/quffka/quffka

공식 코드

Quadrature-based features for kernel approximation

https://github.com/samjia2000/optimal-reasoning-efficiency

공식 코드

How Far Are We from Optimal Reasoning Efficiency?

https://github.com/samuelhocking/pinvar-sisc-2024

공식 코드

Physics-informed nonlinear vector autoregressive models for the prediction of dynamical systems

https://github.com/sangyun884/rfpp

공식 코드

Improving the Training of Rectified Flows

https://github.com/sbnietert/sliced-wp

공식 코드

Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances

https://github.com/thoglu/jammy_flows

공식 코드

Unifying supervised learning and VAEs -- coverage, systematics and goodness-of-fit in normalizing-flow based neural network models for astro-particle reconstructions

https://github.com/tk-rusch/mpmc

공식 코드

Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks

https://github.com/torekleppe/PDPHMCpaperCode

공식 코드

Connecting the Dots: Numerical Randomized Hamiltonian Monte Carlo with State-Dependent Event Rates

https://github.com/veronicasaz/planetarysystem_hnn

공식 코드

A hybrid approach for solving the gravitational N-body problem with Artificial Neural Networks

https://github.com/westny/neural-stability

공식 코드

Stability-Informed Initialization of Neural Ordinary Differential Equations

https://github.com/wuwushrek/taylanets

공식 코드

Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs

https://github.com/xxh0523/Py_PSNODE

공식 코드

Feasibility Study of Neural ODE and DAE Modules for Power System Dynamic Component Modeling

https://github.com/yuyudeep/hcmt

공식 코드

Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer

https://gitlab.com/achatali/efficient-numerical-integration-in-rkhs-via-ls-sampling

공식 코드

Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling

https://gitlab.com/felix.dietrich/sde-identification

공식 코드

Learning effective stochastic differential equations from microscopic simulations: linking stochastic numerics to deep learning

https://gitlab.com/i-flow/i-flow

공식 코드

i-flow: High-dimensional Integration and Sampling with Normalizing Flows

https://gitlab.com/nicolasevangelou/ml_phase_transition

공식 코드

Machine Learning for the identification of phase-transitions in interacting agent-based systems: a Desai-Zwanzig example

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.