| Algorithm | discover |
|---|---|
| Dataset | chafee-infante |
| Expression | \( u_{t} = 0.9808 t^{3} u + 0.9808 u^{3} - 0.9946 u + 0.9754 u_{xx} \) |
| Best reward | 0.955317 |
| R² | 0.999764 |
| NMSE | 0.0002362 |
| Iterations | 200 |
| Early Stopped | False |
| config.algorithm | discover |
| config.n_iterations | 100 |
| config.seed | 42 |
| config.library | {'operators': ['add', 'mul', 'div', 'diff_x', 'diff2_x', 'diff3_x', 'n2', 'n3'], 'state_vars': ['u'], 'coord_vars': ['x', 't']} |
| config.min_length | 2 |
| config.max_length | 256 |
| config.batch_size | 500 |
| config.num_units | 32 |
| config.num_layers | 1 |
| config.embedding_dim | 4 |
| config.observe_parent | True |
| config.observe_sibling | True |
| config.observe_action | False |
| config.observe_dangling | False |
| config.use_embedding | False |
| config.attention | True |
| config.attn_length | 10 |
| config.initializer | zeros |
| config.epsilon | 0.02 |
| config.baseline | R_e |
| config.entropy_weight | 0.03 |
| config.gamma | 0.5 |
| config.reward_alpha | 0.01 |
| config.learning_rate | 0.0025 |
| config.entropy_gamma | 0.7 |
| config.max_diff_order | 4 |
| config.use_repeat_prior | True |
| config.repeat_tokens | ['add'] |
| config.repeat_max | 5 |
| config.use_trig_prior | True |
| config.use_inverse_prior | True |
| config.use_diff_descendant_prior | True |
| config.use_diff_child_prior | True |
| config.soft_length_loc | 10.0 |
| config.soft_length_scale | 5.0 |
| config.stability_selection | 0 |
| config.stability_queue_capacity | 10 |
| config.magnitude_filter | False |
| config.diagnostic_scaffold | False |
| config.diagnostic_scaffold_diffusion_tokens | [] |
| config.diagnostic_scaffold_reaction_tokens | [] |
| config.diagnostic_scaffold_root_tokens | ['add', 'sub'] |
| config.diagnostic_scaffold_neutral_tokens | [] |
| config.token_bias_tokens | [] |
| config.token_bias_weight | 0.0 |
| config.pinn | None |
| config.provider_kind | finite_diff |
Read the best native score reached at each search iteration.
Compare predicted and observed targets against the diagonal of exact agreement.
Read the published equation with its fitted coefficients and active terms.
Read the operators and derivatives that compose the published equation.
Read the distribution and spatial pattern of predicted minus observed target values.
Compare the magnitudes and signs of the active fitted coefficients.
Compare the measured field with forward integration of the discovered equation and their difference.
Compare the fitted derivative target with the equation prediction and their residual, without forward integration.
Compare measured and integrated fields at the labeled observation times.
Locate the signed field error from forward integration at the labeled physical coordinates.
Per-iteration mean reward of the top-ε risk-seeking subset the policy gradient trains on — kd's analogue of what the reference implementation calls 'Mean of Top ε' (r_avg_sub; kd selects the subset after invalid-row filtering, delta D-019, so the subsets can differ for larger batches), NOT the batch reward. Under the default batch=16, ε=0.05 the subset is a single sample, so this curve equals the per-iteration maximum, and with the default 'R_e' baseline it also coincides with the Reward Baseline plot by construction. The whole-batch signal is the 'Reward (Full-Batch Mean)' plot.
Per-iteration mean reward over the FULL sampled batch (invalid expressions carry the invalid-reward fill) — the reference implementation's r_avg_full. Unlike the top-ε curve this one moves with the whole policy, so the two panels together show elite vs population progress.
Entropy regularizer loss component (= -weight x mean policy entropy, typically negative). Direction-neutral title on purpose: the curve RISES toward zero as the policy's entropy collapses — it does not 'decay' for a healthy run.
Scalar reward baseline used by the RSPG policy-gradient estimator. NOTE: the trailing ``_ewma`` in the plot name is a legacy artifact retained for backward compatibility with an earlier plot-naming scheme — the actual series semantics depend on ``RSPGStrategy.baseline``: ``'R_e'`` (default) is a risk-seeking quantile, ``'ewma_R'`` is an exponential moving average, ``'combined'`` sums both. Read the configured baseline mode before interpreting the curve; the human-readable plot title (``Reward Baseline``) is mode-neutral on purpose.
{
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"actual": "[60200 elements]",
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"dataset_name": "chafee-infante",
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"record_hash_scheme": "kd-record-v1"
},
"score_kind": "reward",
"score_direction": "max"
}