5 件 ・ 関連度順
概要と使いどころ
Star schema, snowflake schema, SCD types, and Kimball methodology for analytical data modeling. Activate on: dimensional model, star schema, snowflake schema, SCD, fact table, dimension table, Kimball, grain, surrogate key. NOT for: dbt implementation (use dbt-analytics-engineer), warehouse tuning (use data-warehouse-optimizer).
日本語の概要は準備中です。原文の説明を表示しています。
Star schema, snowflake schema, SCD types, and Kimball methodology for analytical data modeling. Activate on: dimensional model, star schema, snowflake schema, SCD, fact table, dimension table, Kimball, grain, surrogate key. NOT for: dbt implementation (use dbt-analytics-engineer), warehouse tuning (use data-warehouse-optimizer).
日本語の概要は準備中です。原文の説明を表示しています。
```yaml name: cognitive-surrogate description: Layer 6 Barton Cognitive Surrogate System - build, train, and validate psychological models that predict and generate authentic responses matching a subject's cognitive patterns version: 1.0.0 trit: 0 # Ergodic/coordinator role in GF(3) triadic system ```
日本語の概要は準備中です。原文の説明を表示しています。
Official NVIDIA-authored guidance for navigating PhysicsNeMo — pick the model, datapipe, or example for a SciML/AI4Science task (surrogates, forecasting, downscaling, physics-informed, inverse, generative). Points at existing files via live repo search; never writes code. Do NOT use for installation or environment setup, training-loop or other code authoring/scaffolding, contributor/CI/packaging questions, repo-specific questions in physicsnemo-sym/-cfd/-curator, or general (non-physics) ML/PyTorch.
日本語の概要は準備中です。原文の説明を表示しています。
Train neural operators (FNO, DeepONet) to learn solution maps for parametric PDE families. Once trained, solve new PDE instances in milliseconds. Use when you need to solve many instances of the same PDE with different parameters/ICs/BCs.
日本語の概要は準備中です。原文の説明を表示しています。