Home › Entities › academia › Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data

Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data

Research article (Machine Learning Science and Technology, 2022) · cited 21× · AI/ML
academia openalex:W4225835446
Press Enter · cited answer in seconds

Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data

Summary

Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data is a scholarly article[1].

Key Facts

  • Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data's instance of is recorded as scholarly article[2].

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

📑 Cite this page

Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data. Retrieved May 24, 2026, from https://4ort.xyz/entity/easy-representation-of-multivariate-functions-with-low-dimensional-terms-via-gaussian-process-regression-kernel-design-a
MLA “Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/easy-representation-of-multivariate-functions-with-low-dimensional-terms-via-gaussian-process-regression-kernel-design-a.
BibTeX @misc{4ortxyz_easy-representation-of-multivariate-functions-with-low-dimensional-terms-via-gaussian-process-regression-kernel-design-a_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data}}, year = {2026}, url = {https://4ort.xyz/entity/easy-representation-of-multivariate-functions-with-low-dimensional-terms-via-gaussian-process-regression-kernel-design-a}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data — https://4ort.xyz/entity/easy-representation-of-multivariate-functions-with-low-dimensional-terms-via-gaussian-process-regression-kernel-design-a (retrieved 2026-05-24)

Canonical URL: https://4ort.xyz/entity/easy-representation-of-multivariate-functions-with-low-dimensional-terms-via-gaussian-process-regression-kernel-design-a · Last refreshed: