sindy-identification

SINDy — Sparse Identification of Nonlinear Dynamics

Overview

Discover governing ODEs from time-series data using SINDy (Brunton et al., PNAS 2016). Given measurements of state variables x(t), SINDy identifies the sparse set of terms in a library of candidate functions that best describe dx/dt.

When to Use

• You have trajectory data and want to find the governing ODE
• System is expected to have a sparse representation (few active terms)
• Input variables are known (you measured the right state variables)
• Data is relatively clean (or you can denoise it)

Do NOT Use When

• You want arbitrary symbolic expressions (use symbolic-regression with PySR)
• You only have steady-state data (SINDy needs time derivatives)
• System is stochastic (SINDy assumes deterministic dynamics)
• You have PDE data (use PDE-FIND variant, not standard SINDy)

Installation

pip install pysindy

Core Workflows

1. Basic SINDy (Discover ODE from Data)

import numpy as np
import pysindy as ps
from scipy.integrate import solve_ivp
import matplotlib.pyplot as plt

# Generate training data from Lorenz system (ground truth)
def lorenz(t, y, sigma=10, rho=28, beta=8/3):
    return [sigma*(y[1]-y[0]), y[0]*(rho-y[2])-y[1], y[0]*y[1]-beta*y[2]]

dt = 0.001
t_train = np.arange(0, 10, dt)
sol = solve_ivp(lorenz, (0, 10), [1, 1, 1], t_eval=t_train, rtol=1e-10)
x_train = sol.y.T  # shape (N, 3)

# Fit SINDy model
model = ps.SINDy(
    feature_names=["x", "y", "z"],
    optimizer=ps.STLSQ(threshold=0.1),  # sparsity threshold
    feature_library=ps.PolynomialLibrary(degree=2),
)
model.fit(x_train, t=dt)
model.print()

# Expected output:
# x' = -10.000 x + 10.000 y
# y' = 28.000 x + -1.000 y + -1.000 x z
# z' = -2.667 z + 1.000 x y

2. Choosing the Sparsity Threshold

# Sweep thresholds and check model complexity vs error
thresholds = [0.001, 0.01, 0.05, 0.1, 0.5, 1.0]
for thresh in thresholds:
    model_t = ps.SINDy(
        optimizer=ps.STLSQ(threshold=thresh),
        feature_library=ps.PolynomialLibrary(degree=2),
    )
    model_t.fit(x_train, t=dt)
    complexity = model_t.complexity  # number of nonzero terms
    # Simulate and compute error
    x_sim = model_t.simulate(x_train[0], t_train)
    rmse = np.sqrt(np.mean((x_sim - x_train)**2))
    print(f"threshold={thresh:.3f}: {complexity} terms, RMSE={rmse:.4f}")

3. Custom Function Libraries

# Include trigonometric functions for oscillatory systems
library = ps.PolynomialLibrary(degree=2) + ps.FourierLibrary(n_frequencies=3)

# Or build a custom library
import pysindy as ps
custom_library = ps.CustomLibrary(
    library_functions=[
        lambda x: x,           # linear
        lambda x: x**2,        # quadratic
        lambda x: np.sin(x),   # sinusoidal
        lambda x: np.cos(x),
    ],
    function_names=[
        lambda x: x,
        lambda x: f"{x}^2",
        lambda x: f"sin({x})",
        lambda x: f"cos({x})",
    ]
)

model = ps.SINDy(
    feature_library=custom_library,
    optimizer=ps.STLSQ(threshold=0.1),
)
model.fit(x_train, t=dt)
model.print()

4. Noisy Data (Smoothed Differentiation)

# Add noise
x_noisy = x_train + 0.1 * np.random.randn(*x_train.shape)

# Use smoothed finite differences for derivatives
model_noisy = ps.SINDy(
    differentiation_method=ps.SmoothedFiniteDifference(),
    optimizer=ps.STLSQ(threshold=0.2),  # higher threshold for noisy data
    feature_library=ps.PolynomialLibrary(degree=2),
)
model_noisy.fit(x_noisy, t=dt)
model_noisy.print()

5. Validate: Simulate and Compare

# Simulate discovered model from same IC
x_sim = model.simulate(x_train[0], t_train)

fig, axes = plt.subplots(3, 1, figsize=(10, 8), sharex=True)
labels = ['x', 'y', 'z']
for i, ax in enumerate(axes):
    ax.plot(t_train, x_train[:, i], 'b-', linewidth=0.5, label='Ground truth')
    ax.plot(t_train, x_sim[:, i], 'r--', linewidth=0.5, label='SINDy model')
    ax.set_ylabel(labels[i], fontsize=13)
    ax.legend(loc='upper right')
    ax.grid(True, alpha=0.3)
axes[-1].set_xlabel('Time [s]')
axes[0].set_title('SINDy Model vs Ground Truth')
plt.tight_layout()
plt.savefig('sindy_validation.png', dpi=150, bbox_inches='tight')

# Quantitative error
rmse = np.sqrt(np.mean((x_sim - x_train)**2, axis=0))
print(f"RMSE per variable: x={rmse[0]:.4f}, y={rmse[1]:.4f}, z={rmse[2]:.4f}")

6. Coefficient Extraction

# Get coefficient matrix (rows = equations, cols = library functions)
coefficients = model.coefficients()
feature_names = model.get_feature_names()

print("Discovered equations:")
for i, eq_name in enumerate(['dx/dt', 'dy/dt', 'dz/dt']):
    terms = []
    for j, name in enumerate(feature_names):
        if abs(coefficients[i, j]) > 1e-10:
            terms.append(f"{coefficients[i,j]:+.3f}·{name}")
    print(f"  {eq_name} = {' '.join(terms)}")

Key Parameters

Parameter	Default	Description
threshold (STLSQ)	0.1	Sparsity threshold — coefficients below this are zeroed
degree (PolynomialLibrary)	2	Maximum polynomial degree in library
alpha (STLSQ)	0.05	Ridge regularization strength
max_iter (STLSQ)	20	Maximum iterations for thresholding

Tips

1. Start with polynomial degree 2 — most physics ODEs are low-order polynomial
2. Threshold tuning is critical — too low = overfitting, too high = missing terms
3. Clean data first — SINDy is sensitive to noise in derivatives
4. Check the library — if the true terms aren't in the library, SINDy can't find them
5. Validate by simulation — a good SINDy model should reproduce the trajectory

Troubleshooting

Symptom	Fix
Too many terms discovered	Increase threshold
Missing terms	Decrease threshold or check library contains needed functions
Poor simulation accuracy	Data too noisy — use SmoothedFiniteDifference
Model diverges on simulation	Discovered model is unstable — check coefficient signs
x_train wrong shape	Must be (n_samples, n_features), NOT (n_features, n_samples)