API reference

Every public name in the package, each linking to its full signature and parameters. kd itself is flat: it is kd.load_kdv, not kd.datasets.load_kdv. The batch loop is the one group imported from a subpackage, kd.harness.

Descriptions come from the library's own docstrings, cut to reference length. Where a parameter carries more than a sentence of contract, the guide pages under Guide hold it: resume tiers in Parameter tiers and resuming, what the scores mean in Score conventions, term syntax in Equation representation.

Model and result

Name	Summary
`kd.Model(algorithm, generations[, ...])`	High-level facade for PDE discovery (PySR-style API).
`kd.ExperimentResult(best_expression, best_score[, ...])`	Serializable value object for a completed experiment.

Algorithm settings

Name	Summary
`kd.instrument_schemas()`	Return agent-facing schemas for facade plugins in registration order.
`kd.SGAConfig(num, p_var[, ...])`	Configuration for the SGA search algorithm.
`kd.DLGAConfig(mode, library[, ...])`	DLGA Stage I (constant-coefficient) configuration.
`kd.DiscoverConfig(n_iterations, seed[, ...])`	Configuration for a DISCOVER (RL + optional PINN) search run.
`kd.EqGPTConfig(sparsity_alpha, seed[, ...])`	Static configuration for the EqGPT plugin.
`kd.Llm4edConfig(temperature, max_tokens[, ...])`	Static configuration for the LLM4ED plugin.
`kd.PySRConfig(terms, seed[, ...])`	Frozen configuration for a PySR symbolic-regression run.
`kd.PySINDyConfig(terms, threshold[, ...])`	Frozen configuration for a PySINDy STLSQ fit.

Datasets

Name	Summary
`kd.PDEDataset(name, task_type[, ...])`	Complete PDE dataset specification.
`kd.TabularDataset(X, y[, ...])`	A scalar regression dataset: features `X`, target `y`, metadata.
`kd.FieldData(name, values[, ...])`	Field data container.
`kd.AxisInfo(name, values[, ...])`	Coordinate axis information.
`kd.DataTopology(value, names[, ...])`	Data layout topology.
`kd.TaskType(value, names[, ...])`	Type of discovery task.
`kd.DatasetReport(name, topology[, ...])`	Structured result of `kd.preview_report` (JSON-dumpable, answer-blind).
`kd.FieldReport(name, dtype[, ...])`	Per-field facts of one dataset field.
`kd.AxisReport(name, n[, ...])`	Per-axis facts of one dataset axis.

Dataset catalog

Name	Summary
`kd.DatasetSpec(id, loader[, ...])`	Static metadata and loader pointer for a discoverable dataset.
`kd.list_datasets()`	Return built-in dataset specs sorted by stable dataset id.
`kd.list_datasets_answer_blind()`	Return the catalog as JSON-safe rows with the ground truth withheld.
`kd.get_dataset(dataset_id)`	Return the dataset spec for `dataset_id`.
`kd.DATASET_CATALOG`	—
`kd.list_remote_datasets()`	Return remote dataset specs sorted by stable dataset id.
`kd.load_from_hub(dataset_id, cache_dir[, ...])`	Fetch `dataset_id` from Hugging Face Hub and return a `PDEDataset`.
`kd.generate_advection_data(speeds, waves[, ...])`	Generate synthetic advection equation data.
`kd.generate_burgers_data(nx, nt[, ...])`	Generate synthetic Burgers equation data.
`kd.generate_diffusion_data(alpha, waves[, ...])`	Generate synthetic diffusion equation data.
`kd.load_kdv(data_dir)`	Load KdV (Korteweg-de Vries) equation dataset.
`kd.load_burgers(data_dir)`	Load the Burgers equation reference dataset from SGA-PDE (Chen et al.).
`kd.load_burgers_2d(data_dir)`	Load the EqGPT 2D viscous Burgers benchmark (.mat), a 2+1D dataset.
`kd.load_chafee_infante(data_dir)`	Load Chafee-Infante equation dataset.
`kd.load_wave(data_dir)`	Load the wave equation benchmark (EqGPT), a second-order LHS dataset.
`kd.load_klein_gordon(data_dir)`	Load the Klein-Gordon benchmark (EqGPT), a second-order LHS dataset.
`kd.load_allen_cahn(data_dir)`	Load the Allen-Cahn reaction-diffusion benchmark (EqGPT).
`kd.load_convection_diffusion(data_dir)`	Load the convection-diffusion benchmark (EqGPT).
`kd.load_eq_6_2_12(data_dir)`	Load the EqGPT Eq. 6.2.12 mixed-derivative benchmark (CSV).
`kd.load_pde_compound(data_dir)`	Load PDE_compound (Eq. S5 from SGA-PDE paper).
`kd.load_pde_divide(data_dir)`	Load PDE_divide (Eq. S4 from SGA-PDE paper).
`kd.load_llm4ed_heat(cache_dir, offline)`	Load the LLM4ED heat-equation mirror dataset from Hugging Face Hub.
`kd.load_llm4ed_fisher(cache_dir, offline)`	Load the LLM4ED Fisher mirror dataset from Hugging Face Hub.
`kd.load_llm4ed_fisher_nonlinear(cache_dir, offline)`	Load the LLM4ED nonlinear-Fisher mirror dataset from Hugging Face Hub.
`kd.load_tlc_cc(target)`	Load the TLC-CC chromatography dataset (real-world experimental data).
`kd.load_wave_breaking(case, data_dir)`	Load wave-tank surface-elevation data (real-world experimental data).

Terms and equations

Name	Summary
`kd.evaluate_terms(dataset, terms[, ...])`	Evaluate candidate terms against a dataset, fail-loud.
`kd.validate_terms(dataset, terms[, ...])`	Classify each term as valid / rejected WITHOUT fitting.
`kd.verify_equation(eq, executor[, ...])`	Verify an equation on a dataset using its reported coefficients unchanged.
`kd.law_signature(eq)`	Build a versioned signature from an equation's active law.
`kd.Sketch(lhs_spec, vocabulary[, ...])`	A versioned evolution-law template with pinned, anchored, and hole clauses.
`kd.EvaluationResult(mse, nmse[, ...])`	Result from evaluating an expression or term list.
`kd.TermValidationReport(results, valid[, ...])`	Structured result of `validate_terms` (JSON-dumpable, no fitting).
`kd.TermRejection(term, reason)`	A single rejected term and the reason it was rejected.
`kd.VerificationReport(signature, form[, ...])`	JSON-serializable measurements for one equation on one dataset.
`kd.VerifyPolicy(nmse_max, coeff_atol[, ...])`	Thresholds used when reporting and comparing empirical verification.
`kd.InvalidTermsError(rejected)`	Raised when one or more terms are rejected and cannot be fitted.
`kd.EvaluationFailedError`	Raised when the final fit is invalid (solver failure / non-finite MSE).

Figures

Name	Summary
`kd.preview(dataset, file)`	Print a sanity-check summary of a `PDEDataset`.
`kd.preview_report(dataset)`	Build a typed report of a `PDEDataset` without printing anything.
`kd.VizEngine(output_dir, style)`	Orchestrates rendering of universal, plugin, and comparison plots.

Checkpoints

Name	Summary
`kd.load_checkpoint_manifest(directory)`	Load + fully verify a directory's manifest, returning entries in order.
`kd.CheckpointManifestEntry(filename, kind[, ...])`	A frozen, JSON-safe manifest entry (`kd-ckptman-v1`).
`kd.CheckpointManifestError`	Raised on any `kd-ckptman-v1` manifest contract violation.
`kd.KIND_FINAL`	—
`kd.FINAL_STATUS_COMPLETED`	—

Batch experiments

Name	Summary
`kd.harness.ExperimentPlan(name, entries)`	An ordered execution matrix of plan entries with a content identity.
`kd.harness.PlanEntry(instrument, dataset_ref[, ...])`	One declarative episode: an instrument applied to a dataset at a seed.
`kd.harness.run_plan(plan, datasets[, ...])`	Pre-flight, then run every plan entry serially into a fresh store.
`kd.harness.PlanRunResult(store_root, outcomes)`	The outcome of a whole batch: the store root plus every episode outcome.
`kd.harness.EpisodeOutcome(entry_index, entry[, ...])`	The sealed result of running one plan entry.
`kd.harness.EvidenceStore(root, plan[, ...])`	A fresh-dir, append-only, tamper-evident batch evidence directory.
`kd.harness.build_consensus(store, datasets[, ...])`	Aggregate a sealed evidence store into a consensus report tree.
`kd.harness.ConsensusReport(provenance, datasets[, ...])`	The full consensus report tree for one sealed evidence store.
`kd.harness.render_consensus_markdown(report)`	Render the report as a deterministic Markdown document (pure function).
`kd.harness.SKETCH_SIDECAR_FILENAME`	—

Surrogate models

Name	Summary
`kd.models.save_field_model(model, path)`	Write `model` as a `kd-surrogate-v1` file and return its path.
`kd.models.load_field_model(path)`	Rebuild the `FieldModel` a `kd-surrogate-v1` file describes.

Plugin interfaces

Name	Summary
`kd.search.SurrogateTrainer(args, kwargs)`	Plugin face for training the derivative surrogate as a standalone action.