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.
Benchmarks
These leaderboards are used to track progress in Numerical Integration
Latest papers
Scientific Computing Algorithms to Learn Enhanced Scalable Surrogates for Mesh Physics
CCSI-Toolset/MGN
•
•
With this, we were able to train MGN on meshes with \textit{millions} of nodes to generate computational fluid dynamics (CFD) simulations.
Automatically Bounding the Taylor Remainder Series: Tighter Bounds and New Applications
google/autobound
•
•
We then recursively combine the bounds for the elementary functions using an interval arithmetic variant of Taylor-mode automatic differentiation.
Learning Integrable Dynamics with Action-Angle Networks
ameya98/actionanglenetworks
•
•
Here, we propose an alternative construction for learned physical simulators that are inspired by the concept of action-angle coordinates from classical mechanics for describing integrable systems.
Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances
The goal of this work is to quantify this scalability from three key aspects: (i) empirical convergence rates; (ii) robustness to data contamination; and (iii) efficient computational methods.
Continuous Mixtures of Tractable Probabilistic Models
AlCorreia/cm-tpm
•
•
Meanwhile, tractable probabilistic models such as probabilistic circuits (PCs) can be understood as hierarchical discrete mixture models, and thus are capable of performing exact inference efficiently but often show subpar performance in comparison to continuous latent-space models.
On Numerical Integration in Neural Ordinary Differential Equations
aiqing-zhu/imde
•
•
The combination of ordinary differential equations and neural networks, i. e., neural ordinary differential equations (Neural ODE), has been widely studied from various angles.
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
ma921/basq
•
•
Empirically, we find that our approach significantly outperforms the sampling efficiency of both state-of-the-art BQ techniques and Nested Sampling in various real-world datasets, including lithium-ion battery analytics.
Discretization Invariant Networks for Learning Maps between Neural Fields
With the emergence of powerful representations of continuous data in the form of neural fields, there is a need for discretization invariant learning: an approach for learning maps between functions on continuous domains without being sensitive to how the function is sampled.
Sampling-free Inference for Ab-Initio Potential Energy Surface Networks
n-gao/pesnet
•
•
In this work, we address the inference shortcomings by proposing the Potential learning from ab-initio Networks (PlaNet) framework, in which we simultaneously train a surrogate model in addition to the neural wave function.
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System
As the topology of the power system is in the form of graph structure, graph neural network based representation learning is naturally suitable for learning the status of the power system.
