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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 101–150 of 242 papers

TitleStatusHype
Bayesian Numerical Integration with Neural NetworksCode0
Analysis of Numerical Integration in RNN-Based Residuals for Fault Diagnosis of Dynamic Systems—0
Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence—0
Some of the variables, some of the parameters, some of the times, with some physics known: Identification with partial information—0
A Closed-form Expression for the Gaussian Noise Model in the Presence of Raman Amplification—0
Implementation and (Inverse Modified) Error Analysis for implicitly-templated ODE-nets—0
Inverse Cubature and Quadrature Kalman filters—0
Symbolic-Numeric Computation of Integrals in Successive Galerkin Approximation of Hamilton-Jacobi-Bellman Equation—0
Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning models—0
D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory—0
Semi-Analytical Electromagnetic Transient Simulation Using Differential Transformation—0
Fitting mixed logit random regret minimization models using maximum simulated likelihood—0
Estimating and Assessing Differential Equation Models with Time-Course Data—0
LiFe-net: Data-driven Modelling of Time-dependent Temperatures and Charging Statistics Of Tesla's LiFePo4 EV Battery—0
Pricing Bermudan Swaption under Two Factor Hull-White Model with Fast Gauss Transform—0
A Neural ODE Interpretation of Transformer Layers—0
Bayesian Experimental Design for Symbolic Discovery—0
A Quantum-Powered Photorealistic Rendering—0
Geometry-preserving Lie Group Integrators For Differential Equations On The Manifold Of Symmetric Positive Definite Matrices—0
Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein DistancesCode0
Optimization-Informed Neural Networks—0
ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitives—0
Novel predator-prey model admitting exact analytical solution—0
On Numerical Integration in Neural Ordinary Differential EquationsCode0
Discretization Invariant Networks for Learning Maps between Neural FieldsCode0
Implicit Function Theorem: Estimates on the size of the domain—0
Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNsCode0
Flow-based density of states for complex actions—0
What ODE-Approximation Schemes of Time-Delay Systems Reveal about Lyapunov-Krasovskii Functionals—0
Splitting numerical integration for matrix completion—0
Fenrir: Physics-Enhanced Regression for Initial Value ProblemsCode0
Inertial Navigation Using an Inertial Sensor ArrayCode0
Mars Entry Trajectory Planning with Range Discretization and Successive Convexification—0
Small-Signal Stability Analysis of Numerical Integration Methods—0
Learned Cone-Beam CT Reconstruction Using Neural Ordinary Differential Equations—0
Machine learning prediction for mean motion resonance behaviour -- The planar case—0
Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEsCode0
`Next Generation' Reservoir Computing: an Empirical Data-Driven Expression of Dynamical Equations in Time-Stepping Form—0
Invariant Priors for Bayesian Quadrature—0
A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel—0
Small-Signal Stability Techniques for Power System Modal Analysis, Control, and Numerical Integration—0
Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing—0
Least-Squares Neural Network (LSNN) Method For Scalar Nonlinear Hyperbolic Conservation Laws: Discrete Divergence Operator—0
Extracting Dynamical Models from Data—0
Efficient Modeling of Morphing Wing Flight Using Neural Networks and Cubature Rules—0
Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit—0
Learning Dynamics from Noisy Measurements using Deep Learning with a Runge-Kutta Constraint—0
Data-based stochastic modeling reveals sources of activity bursts in single-cell TGF-β signalingCode0
Revisiting the Effects of Stochasticity for Hamiltonian Samplers—0
Distributional Gradient Matching for Learning Uncertain Neural Dynamics ModelsCode0
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