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Numerical Integration

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

Papers

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Showing 26–50 of 242 papers

TitleStatusHype
Graph Spring Neural ODEs for Link Sign Prediction—0
Importance Sampling With Stochastic Particle Flow and Diffusion Optimization—0
Data-Augmented Numerical Integration in State Prediction: Rule Selection—0
Modular addition without black-boxes: Compressing explanations of MLPs that compute numerical integration—0
Beyond Monte Carlo: Harnessing Diffusion Models to Simulate Financial Market Dynamics—0
Adaptive Process-Guided Learning: An Application in Predicting Lake DO ConcentrationsCode0
Coupled Integral PINN for conservation law—0
Stationary Velocity Fields on Matrix Groups for Deformable Image Registration—0
Neurally Integrated Finite Elements for Differentiable Elasticity on Evolving Domains—0
GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systemsCode1
Spectral Densities, Structured Noise and Ensemble Averaging within Open Quantum Dynamics—0
Average Causal Effect Estimation in DAGs with Hidden Variables: Extensions of Back-Door and Front-Door CriteriaCode0
Modelling the age distribution of longevity leaders—0
Correlation Properties in Channels with von Mises-Fisher Distribution of Scatterers—0
Domain-decoupled Physics-informed Neural Networks with Closed-form Gradients for Fast Model Learning of Dynamical Systems—0
Physics-informed nonlinear vector autoregressive models for the prediction of dynamical systemsCode0
Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning—0
An Efficient Approach to Regression Problems with Tensor Neural Networks—0
Flow map matching with stochastic interpolants: A mathematical framework for consistency models—0
A novel robust meta-analysis model using the t distribution for outlier accommodation and detection—0
Improving the Training of Rectified FlowsCode2
Exploration of methods for computing sensitivities in ODE models at dynamic and steady states—0
Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural NetworksCode1
Identifiability of Differential-Algebraic SystemsCode0
Evaluating AI-generated code for C++, Fortran, Go, Java, Julia, Matlab, Python, R, and RustCode0
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