Numerical Integration
53 papers with code • 0 benchmarks • 0 datasets
Numerical integration is the task to calculate the numerical value of a definite integral or the numerical solution of differential equations.
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Latest papers with no code
Auto-Calibration and 2D-DOA Estimation in UCAs via an Integrated Wideband Dictionary
In this paper, we present a novel auto-calibration scheme for the joint estimation of the two-dimensional (2-D) direction-of-arrival (DOA) and the mutual coupling matrix (MCM) for a signal measured using uniform circular arrays.
Probabilistic Numeric SMC Sampling for Bayesian Nonlinear System Identification in Continuous Time
To address this issue, the field of probabilistic numerics combines numerical methods, such as numerical integration, with probabilistic modeling to offer a more comprehensive analysis of total uncertainty.
Inverse Nonlinearity Compensation of Hyperelastic Deformation in Dielectric Elastomer for Acoustic Actuation
This paper delves into the analysis of nonlinear deformation induced by dielectric actuation in pre-stressed ideal dielectric elastomers.
Rethinking Directional Integration in Neural Radiance Fields
To that end, we introduce a modification to the NeRF rendering equation which is as simple as a few lines of code change for any NeRF variations, while greatly improving the rendering quality of view-dependent effects.
Machine Learning for the identification of phase-transitions in interacting agent-based systems
Deriving closed-form, analytical expressions for reduced-order models, and judiciously choosing the closures leading to them, has long been the strategy of choice for studying phase- and noise-induced transitions for agent-based models (ABMs).
On discretisation drift and smoothness regularisation in neural network training
We find that smoothness regularisation affects optimisation across multiple deep learning domains, and that incorporating smoothness regularisation in reinforcement learning leads to a performance boost that can be recovered using adaptions to optimisation methods.
Towards Hyperparameter-Agnostic DNN Training via Dynamical System Insights
We present a stochastic first-order optimization method specialized for deep neural networks (DNNs), ECCO-DNN.
Learning nonlinear integral operators via Recurrent Neural Networks and its application in solving Integro-Differential Equations
In this paper, we propose using LSTM-RNNs (Long Short-Term Memory-Recurrent Neural Networks) to learn and represent nonlinear integral operators that appear in nonlinear integro-differential equations (IDEs).
Time-vectorized numerical integration for systems of ODEs
This paper describes efficient, implicit, vectorized methods for integrating stiff systems of ordinary differential equations through time and calculating parameter gradients with the adjoint method.
Adaptive approximation of monotone functions
We prove that GreedyBox achieves an optimal sample complexity for any function $f$, up to logarithmic factors.
