condition-setup

# Set Up Conditions for a PINA Problem > [!IMPORTANT] > Read [RULES.md](../RULES.md) before using this skill — it applies to all skills. > This is a sub-skill of **create-problem**. Load the entry-point skill first. Use this skill to define `Condition` objects that bind data, equations, or time-series windows to the problem. ## Step 1 — Determine the condition type Three kinds of conditions exist in PINA: | Kind | When to use | |-----------------------------|--------------------------------------------------| | **Physics-on-domain** | PDE/ODE residual on a sampled domain | | **Data-driven** | Input→target mapping (supervised) | | **Time series** | Rolling-window forecasting | ## Step 2 — Data types (data-driven only) If the problem is data-driven, ask: > What data type are you using? Available data types for `Condition(input=..., target=...)`: - **`LabelTensor` / `torch.Tensor`** — standard tensor data (most common) - **`Graph`** — PINA's built-in graph structure (from `pina import Graph`) - **`Data`** — PyTorch Geometric `Data` object (from `torch_geometric.data import Data`) All three types are accepted directly as `input`/`target`. ## Step 3 — Build conditions ### Physics-on-domain Conditions map domain names (sampled later via `discretise_domain`) or explicit point tensors to equations: ```python from pina import Condition # Option 1: reference a domain by name (sampled later) conditions = { "boundary": Condition(domain="boundary", equation=FixedValue(0.0)), "interior": Condition(domain="D", equation=Equation(my_pde)), } # Option 2: provide explicit points conditions = { "data_pde": Condition(input=points_tensor, equation=Equation(my_pde)), } ``` ### Data-driven (supervised) ```python conditions = { "data": Condition(input=input_tensor, target=target_tensor), } ``` ### Time series forecasting If the user has time series data, ask whether they want **standard supervised** or **time-series** conditions: ```python from pina import Condition # Standard supervised Condition(input=ts_tensor, target=target_tensor) # Time series (input is 3D: [batch, n_windows, features]) Condition( input=ts_tensor, n_windows=10, unroll_length=5, randomize=True, ) # Graph time series Condition( input=graph_ts_data, n_windows=10, unroll_length=5, key="some_key", ) ``` Parameters: - `n_windows` — number of rolling windows - `unroll_length` — prediction horizon per window - `randomize` — shuffle window order - `key` — key for graph time series data ## Step 4 — Integrate with the problem class Conditions become a class-level dict on the problem: ```python class MyProblem(SpatialProblem): output_variables = ["u"] spatial_domain = CartesianDomain({"x": [0, 1]}) domains = { "D": spatial_domain, "boundary": spatial_domain.partial(), } conditions = { "boundary": Condition(domain="boundary", equation=FixedValue(0.0)), "D": Condition(domain="D", equation=Equation(my_pde)), } ``` ## Checklist - [ ] For data-driven: confirmed data type (`LabelTensor`, `torch.Tensor`, `Graph`, or PyG `Data`) - [ ] For data-driven: `input_variables` is a `list[str]` naming the inputs - [ ] For time series: `n_windows`, `unroll_length`, and optional `key` are set correctly - [ ] Each `Condition` uses valid keyword arguments: - `Condition(domain=..., equation=...)` for physics-on-domain - `Condition(input=..., equation=...)` for physics-on-points - `Condition(input=..., target=...)` for data-driven - `Condition(input=..., n_windows=..., unroll_length=...)` for time series - [ ] `domains` dict has an entry for every domain name used in conditions