arXiv (All)
open access
Pure and physics-guided deep learning approaches for spatio-temporal groundwater level prediction
Groundwater represents a key element of the water cycle, yet it exhibits complex and context-dependent relationships that make its modeling challenging. Theory-based models have been the cornerstone of scientific understanding. However, their computational cost, simplifying assumptions, and calibrat
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Surrogate optimization of deep neural networks for groundwater predictions#620091
- Surrogate optimization of deep neural networks for groundwater predictions#19764
- A Novel Transfer Learning-Based Adaptive Weight Physics-Informed Neural Network for Groundwater Modeling in the Middle Reaches of Heihe River Basin, Northwestern China#381281
- A hybrid deep learning framework for inferring unobserved subsurface water flow across the contiguous United States / Lennart Greiner ; Gutachter: Alexander Brenning, Shijie Jiang#1052369
- A hybrid deep learning framework for inferring unobserved subsurface water flow across the contiguous United States#683267
Record · ID 1141506
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive