Getting Started

This guide walks through installation and a minimal training workflow.

Installation

From the repo root:

python -m pip install -e .

Optional extras:

python -m pip install -e ".[WithMPI]"
python -m pip install -e ".[WithH5py]"

Quickstart

This example trains a small operator model on synthetic data. Arrays are expected in shape (n_samples, n_features).

import numpy as np
import nnopinf
import nnopinf.operators as operators
import nnopinf.models as models
import nnopinf.training

# Synthetic data: simple linear dynamics y = A x
n_samples = 200
state_dim = 3
A = np.array([[0.0, 1.0, 0.0],
              [-1.0, 0.0, 0.5],
              [0.0, -0.5, 0.0]])
x = np.random.randn(n_samples, state_dim)
y = x @ A.T

x_var = nnopinf.Variable(size=state_dim, name="x", normalization_strategy="MaxAbs")
y_var = nnopinf.Variable(size=state_dim, name="y", normalization_strategy="MaxAbs")
x_var.set_data(x)
y_var.set_data(y)

op = operators.StandardOperator(
    n_outputs=state_dim,
    depends_on=(x_var,),
    n_hidden_layers=2,
    n_neurons_per_layer=16,
)
model = models.Model([op])

settings = nnopinf.training.get_default_settings()
settings["num-epochs"] = 100
settings["batch-size"] = 50

nnopinf.training.train(model, variables=[x_var], y=y_var, training_settings=settings)

Notes

• Input variables currently support the normalization strategies Abs and MaxAbs.

• The response variable is normalized internally with a max-abs strategy during training.