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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 201–242 of 242 papers

TitleStatusHype
Inertial Navigation Using an Inertial Sensor ArrayCode0
Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores SamplingCode0
A note on the option price and 'Mass at zero in the uncorrelated SABR model and implied volatility asymptotics'Code0
Efficient time stepping for numerical integration using reinforcement learningCode0
A Dictionary of Closed-Form Kernel Mean EmbeddingsCode0
Provable Quantum Algorithm Advantage for Gaussian Process QuadratureCode0
Stability-Informed Initialization of Neural Ordinary Differential EquationsCode0
Evaluating AI-generated code for C++, Fortran, Go, Java, Julia, Matlab, Python, R, and RustCode0
Quadrature-based features for kernel approximationCode0
On two ways to use determinantal point processes for Monte Carlo integrationCode0
Learning Survival Distribution with Implicit Survival FunctionCode0
Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein DistancesCode0
Designing Stable Neural Networks using Convex Analysis and ODEsCode0
Learning effective stochastic differential equations from microscopic simulations: linking stochastic numerics to deep learningCode0
Bayesian Probabilistic Numerical Integration with Tree-Based ModelsCode0
Bayesian Numerical Integration with Neural NetworksCode0
Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNsCode0
Fenrir: Physics-Enhanced Regression for Initial Value ProblemsCode0
Batch Selection for Parallelisation of Bayesian QuadratureCode0
Outlier-robust neural network training: variation regularization meets trimmed loss to prevent functional breakdownCode0
Learning Nonparametric Volterra Kernels with Gaussian ProcessesCode0
Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEsCode0
Physics-informed nonlinear vector autoregressive models for the prediction of dynamical systemsCode0
Discretization Invariant Networks for Learning Maps between Neural FieldsCode0
Data-based stochastic modeling reveals sources of activity bursts in single-cell TGF-β signalingCode0
Stochastic Latent Transformer: Efficient Modelling of Stochastically Forced Zonal JetsCode0
Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-timeCode0
Machine Learning for the identification of phase-transitions in interacting agent-based systems: a Desai-Zwanzig exampleCode0
Structured Variational Inference in Continuous Cox Process ModelsCode0
Predicting Change, Not States: An Alternate Framework for Neural PDE SurrogatesCode0
Minimizing robust density power-based divergences for general parametric density modelsCode0
Connecting the Dots: Numerical Randomized Hamiltonian Monte Carlo with State-Dependent Event RatesCode0
Combining Normalizing Flows and Quasi-Monte CarloCode0
Adaptive Process-Guided Learning: An Application in Predicting Lake DO ConcentrationsCode0
Calibrating Multivariate Lévy Processes with Neural NetworksCode0
How Far Are We from Optimal Reasoning Efficiency?Code0
Identifiability of Differential-Algebraic SystemsCode0
i-flow: High-dimensional Integration and Sampling with Normalizing FlowsCode0
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEsCode0
Average Causal Effect Estimation in DAGs with Hidden Variables: Extensions of Back-Door and Front-Door CriteriaCode0
Scalable Variational Inference for Dynamical SystemsCode0
A hybrid approach for solving the gravitational N-body problem with Artificial Neural NetworksCode0
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