Prandtl

CFD surrogate modeling toolkit. Train fast aerodynamic surrogates — scikit-learn-like API.

import prandtl as pr

# Sample + learn + predict
X, Y = pr.sample(pr.analytical.cl_flat_plate, bounds=[(-5, 15), (0.01, 0.1)], n=100)
surrogate = pr.Surrogate(params=["alpha", "camber"], outputs=["CL"], method="gp")
surrogate.fit(X, Y)
Y_pred = surrogate.predict(X_test)

# Validate
from prandtl import metrics, cross_validate, learning_curve, residual_analysis

The Problem

CFD simulation: 40 minutes per run.
You need 100+ parameter combinations → 66 hours.

Prandtl: Learn from 100 runs → predict the rest in milliseconds, error < 0.2%.

Key Features

- **Zero CFD Required** Built-in analytical truth functions — validate your pipeline without touching a solver. - **Two Backends** Gaussian Process (GPyTorch) for small-data regimes. MLP (PyTorch) for large-scale and ONNX export. - **Validation Suite** Cross-validation, learning curves, residual analysis — know if your surrogate actually works. - **Physics Constraints** Monotonicity, convexity, boundary values — inject domain knowledge directly into training. - **ONNX Export** Deploy trained surrogates anywhere: edge devices, real-time control loops, cloud. - **CFD I/O** Parse OpenFOAM forces and SU2 history in one line — from solver output to training-ready.

Quick Example

import prandtl as pr
import numpy as np

# 1. Sample the parameter space
X, Y = pr.sample(
    pr.analytical.cl_flat_plate,
    bounds=[(-5, 15), (0.01, 0.1)],
    n=100, method="lhs", seed=42
)

# 2. Train a surrogate
surrogate = pr.Surrogate(
    params=["alpha", "camber"],
    outputs=["CL"],
    method="gp"
)
surrogate.fit(X, Y)

# 3. Predict on new points
X_test, Y_test = pr.sample(
    pr.analytical.cl_flat_plate,
    bounds=[(-5, 15), (0.01, 0.1)],
    n=30, seed=99
)
Y_pred = surrogate.predict(X_test)

# 4. Evaluate
report = pr.metrics(Y_test, Y_pred)
print(report)  # R² > 0.999 on smooth analytical functions

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