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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 1–50 of 242 papers

TitleStatusHype
Force-Free Molecular Dynamics Through Autoregressive Equivariant NetworksCode2
Improving the Training of Rectified FlowsCode2
Automatically Bounding the Taylor Remainder Series: Tighter Bounds and New ApplicationsCode2
Underdamped Diffusion Bridges with Applications to SamplingCode1
FNIN: A Fourier Neural Operator-based Numerical Integration Network for Surface-form-gradientsCode1
GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systemsCode1
Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural NetworksCode1
Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh TransformerCode1
Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh PhysicsCode1
Learning Integrable Dynamics with Action-Angle NetworksCode1
Continuous Mixtures of Tractable Probabilistic ModelsCode1
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel RecombinationCode1
Sampling-free Inference for Ab-Initio Potential Energy Surface NetworksCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
A Frequency Domain Approach to Predict Power System TransientsCode1
Feasibility Study of Neural ODE and DAE Modules for Power System Dynamic Component ModelingCode1
Discovery of Nonlinear Dynamical Systems using a Runge-Kutta Inspired Dictionary-based Sparse Regression ApproachCode1
Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal MemoryCode1
AutoInt: Automatic Integration for Fast Neural Volume RenderingCode1
Learning Mesh-Based Simulation with Graph NetworksCode1
Hierarchical Deep Learning of Multiscale Differential Equation Time-SteppersCode1
Unifying supervised learning and VAEs -- coverage, systematics and goodness-of-fit in normalizing-flow based neural network models for astro-particle reconstructionsCode1
Continuous-in-Depth Neural NetworksCode1
Hamiltonian neural networks for solving equations of motionCode1
Bayesian inference for logistic models using Polya-Gamma latent variablesCode1
Gaussian Processes and Reproducing Kernels: Connections and Equivalences—0
Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models—0
How Far Are We from Optimal Reasoning Efficiency?Code0
ODE-GS: Latent ODEs for Dynamic Scene Extrapolation with 3D Gaussian Splatting—0
Operator-Splitting Methods for Neuromorphic Circuit Simulation—0
Artificial Intelligence for Direct Prediction of Molecular Dynamics Across Chemical Space—0
Continuous-time iterative linear-quadratic regulator—0
Discretization of Linear Systems using the Matrix Exponential—0
A Dictionary of Closed-Form Kernel Mean EmbeddingsCode0
On the Eigenvalue Tracking of Large-Scale Systems—0
Asian Basket Spread Options: A New Approximation Based on Stochastic Taylor Expansions—0
Riemannian Patch Assignment Gradient Flows—0
Neural network-enhanced integrators for simulating ordinary differential equations—0
Computing Modes of Instability of Parameterized Nonlinear Systems for Vulnerability Assessment—0
PI-Controlled Variable Time-Step Power System Simulation Using an Adaptive Order Differential Transformation Method—0
Denoising Hamiltonian Network for Physical Reasoning—0
Provable Quantum Algorithm Advantage for Gaussian Process QuadratureCode0
EFiGP: Eigen-Fourier Physics-Informed Gaussian Process for Inference of Dynamic Systems—0
Lilan: A linear latent network approach for real-time solutions of stiff, nonlinear, ordinary differential equations—0
Median of Means Sampling for the Keister Function—0
Time-Reversible Bridges of Data with Machine Learning—0
Predicting Change, Not States: An Alternate Framework for Neural PDE SurrogatesCode0
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
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