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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/BartvLaatum/GreenLight-Gym2
공식 코드https://github.com/DenisBless/UnderdampedDiffusionBridges
공식 코드https://github.com/ImperialCollegeLondon/BART-Int
공식 코드Bayesian Probabilistic Numerical Integration with Tree-Based Models
https://github.com/PyFE/PyFENG
공식 코드https://github.com/PyFE/PyfengForPapers
공식 코드https://github.com/RunlongYu/April
공식 코드Adaptive Process-Guided Learning: An Application in Predicting Lake DO Concentrations
https://github.com/Wenqi-Cui/Predict-Power-System-Dynamics-Frequency-Domain
공식 코드A Frequency Domain Approach to Predict Power System Transients
https://github.com/afqueiruga/ContinuousNet
공식 코드https://github.com/aiqing-zhu/imde
공식 코드On Numerical Integration in Neural Ordinary Differential Equations
https://github.com/ameya98/actionanglenetworks
공식 코드https://github.com/andrewkirby2/ctstar_statistical_model
공식 코드https://github.com/annaguo-bios/flexcausal
공식 코드https://github.com/anthonyzhou-1/temporal_pdes
공식 코드Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates
https://github.com/bcai0797/isf
공식 코드Learning Survival Distribution with Implicit Survival Function
https://github.com/bokveizen/Feedback-Gradient-Descent
공식 코드Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNs
https://github.com/boxhed/boxhed2.0
공식 코드https://github.com/cagalvisf/quantum_hsgpq
공식 코드Provable Quantum Algorithm Advantage for Gaussian Process Quadrature
https://github.com/charlyandral/qmc_norm_flows
공식 코드https://github.com/clintonjwang/di-net
공식 코드Discretization Invariant Networks for Learning Maps between Neural Fields
https://github.com/computational-imaging/automatic-integration
공식 코드AutoInt: Automatic Integration for Fast Neural Volume Rendering
https://github.com/deepmind/deepmind-research
공식 코드https://github.com/diehlpkpapers/heat-ai
공식 코드Evaluating AI-generated code for C++, Fortran, Go, Java, Julia, Matlab, Python, R, and Rust
https://github.com/fsherry/non-expansive-odes
공식 코드Designing Stable Neural Networks using Convex Analysis and ODEs
https://github.com/gabb7/AReS-MaRS
공식 코드AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs
https://github.com/google/autobound
공식 코드Automatically Bounding the Taylor Remainder Series: Tighter Bounds and New Applications
https://github.com/goyalpike/RK4_SinDy
공식 코드https://github.com/hcarlsso/array-in
공식 코드https://github.com/ibm/trajcast
공식 코드Force-Free Molecular Dynamics Through Autoregressive Equivariant Networks
https://github.com/ira-shokar/stochastic_latent_transformer
공식 코드Stochastic Latent Transformer: Efficient Modelling of Stochastically Forced Zonal Jets
https://github.com/kxchern/dkepool-tsa
공식 코드Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System
https://github.com/lenarttreven/dgm
공식 코드Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models
https://github.com/luckystarufo/multiscale_HiTS
공식 코드Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers
https://github.com/lueckem/quadrature-ML
공식 코드Efficient time stepping for numerical integration using reinforcement learning
https://github.com/ma921/basq
공식 코드Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
https://github.com/magnusross/nvkm
공식 코드Learning Nonparametric Volterra Kernels with Gaussian Processes
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.