Home ›
Entities
› academia
› Forecasting solar-thermal systems performance under transient operation using a data-driven machine learning approach based on the deep operator network architecture
Forecasting solar-thermal systems performance under transient operation using a data-driven machine learning approach based on the deep operator network architecture
Research article (Energy Conversion and Management, 2021) · cited 54× · AI/ML
Forecasting solar-thermal systems performance under transient operation using a data-driven machine learning approach based on the deep operator network architecture
Summary
Forecasting solar-thermal systems performance under transient operation using a data-driven machine learning approach based on the deep operator network architecture is a scholarly article[1].
Key Facts
Forecasting solar-thermal systems performance under transient operation using a data-driven machine learning approach based on the deep operator network architecture's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
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.
APA4ort.xyz Knowledge Graph. (2026). Forecasting solar-thermal systems performance under transient operation using a data-driven machine learning approach based on the deep operator network architecture. Retrieved May 24, 2026, from https://4ort.xyz/entity/forecasting-solar-thermal-systems-performance-under-transient-operation-using-a-data-driven-machine-learning-approach-ba
MLA“Forecasting solar-thermal systems performance under transient operation using a data-driven machine learning approach based on the deep operator network architecture.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/forecasting-solar-thermal-systems-performance-under-transient-operation-using-a-data-driven-machine-learning-approach-ba.
BibTeX@misc{4ortxyz_forecasting-solar-thermal-systems-performance-under-transient-operation-using-a-data-driven-machine-learning-approach-ba_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Forecasting solar-thermal systems performance under transient operation using a data-driven machine learning approach based on the deep operator network architecture}}, year = {2026}, url = {https://4ort.xyz/entity/forecasting-solar-thermal-systems-performance-under-transient-operation-using-a-data-driven-machine-learning-approach-ba}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Forecasting solar-thermal systems performance under transient operation using a data-driven machine learning approach based on the deep operator network architecture — https://4ort.xyz/entity/forecasting-solar-thermal-systems-performance-under-transient-operation-using-a-data-driven-machine-learning-approach-ba (retrieved 2026-05-24)
