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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 1–25 of 242 papers

TitleStatusHype
Automatically Bounding the Taylor Remainder Series: Tighter Bounds and New ApplicationsCode2
Improving the Training of Rectified FlowsCode2
Force-Free Molecular Dynamics Through Autoregressive Equivariant NetworksCode2
Underdamped Diffusion Bridges with Applications to SamplingCode1
Sampling-free Inference for Ab-Initio Potential Energy Surface NetworksCode1
Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal MemoryCode1
Learning Integrable Dynamics with Action-Angle NetworksCode1
FNIN: A Fourier Neural Operator-based Numerical Integration Network for Surface-form-gradientsCode1
Hierarchical Deep Learning of Multiscale Differential Equation Time-SteppersCode1
Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh TransformerCode1
Feasibility Study of Neural ODE and DAE Modules for Power System Dynamic Component ModelingCode1
A Frequency Domain Approach to Predict Power System TransientsCode1
Continuous Mixtures of Tractable Probabilistic ModelsCode1
Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh PhysicsCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
Discovery of Nonlinear Dynamical Systems using a Runge-Kutta Inspired Dictionary-based Sparse Regression ApproachCode1
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel RecombinationCode1
AutoInt: Automatic Integration for Fast Neural Volume RenderingCode1
GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systemsCode1
Hamiltonian neural networks for solving equations of motionCode1
Bayesian inference for logistic models using Polya-Gamma latent variablesCode1
Learning Mesh-Based Simulation with Graph NetworksCode1
Continuous-in-Depth Neural NetworksCode1
Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural NetworksCode1
Unifying supervised learning and VAEs -- coverage, systematics and goodness-of-fit in normalizing-flow based neural network models for astro-particle reconstructionsCode1
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