neural-operator

name	neural-operator
description	Train neural operators (FNO, DeepONet) to learn solution maps for parametric PDE families. Once trained, solve new PDE instances in milliseconds. Use when you need to solve many instances of the same PDE with different parameters/ICs/BCs.
category	physics
version	1.0.0
author	Synthetic Sciences
license	MIT
tags	["Neural Operator","FNO","DeepONet","PDE","Surrogate Model","Deep Learning"]
dependencies	["neuraloperator>=0.3.0","torch>=2.1.0","numpy>=1.24.0","matplotlib>=3.7.0"]

Neural Operators (FNO / DeepONet)

Overview

Neural operators learn mappings between function spaces — given an input function (initial condition, forcing, boundary), they predict the output function (PDE solution). Once trained on a dataset of PDE solutions, they solve new instances in milliseconds.

When to Use

• You need to solve the SAME PDE many times with different parameters
• Real-time predictions needed (design optimization, control)
• Building a surrogate model for expensive simulations
• The PDE family is known but expensive to solve numerically

Do NOT Use When

• Solving a single PDE instance (use pde-solver — faster)
• You don't have training data (solve the PDE a few hundred times first)
• You need high accuracy (< 0.1% error is hard for neural operators)
• The PDE changes fundamentally between instances (different physics)

Installation

pip install neuraloperator torch

Core Workflows

1. Fourier Neural Operator (FNO) — 1D Burgers Equation

import torch
import numpy as np
from neuraloperator.models import FNO1d
from neuraloperator.datasets import load_darcy_flow_small
import matplotlib.pyplot as plt

# Generate training data: Burgers equation solutions
# u_t + u*u_x = nu*u_xx, x in [0, 2pi], periodic BC
from scipy.integrate import solve_ivp
from scipy.fft import fft, ifft, fftfreq

def solve_burgers(u0, nu=0.01, T=1.0, N=256, dt=0.001):
    """Solve Burgers equation using pseudospectral method."""
    dx = 2*np.pi / N
    x = np.linspace(0, 2*np.pi, N, endpoint=False)
    k = fftfreq(N, d=dx) * 2*np.pi

    def rhs(t, u_hat):
        u = np.real(ifft(u_hat))
        u_x = np.real(ifft(1j * k * u_hat))
        return -fft(u * u_x) - nu * k**2 * u_hat

    u0_hat = fft(u0)
    sol = solve_ivp(rhs, (0, T), u0_hat, method='RK45',
                    rtol=1e-8, atol=1e-10)
    return np.real(ifft(sol.y[:, -1]))

# Generate dataset
N = 256
n_train = 500
n_test = 100
x = np.linspace(0, 2*np.pi, N, endpoint=)


np.random.seed()
inputs = []
outputs = []
 i  (n_train + n_test):
    
    u0 = np.zeros(N)
     k  (, ):
        u0 += np.random.randn() * np.sin(k*x) + np.random.randn() * np.cos(k*x)
    u0 *= 
    u_final = solve_burgers(u0)
    inputs.append(u0)
    outputs.append(u_final)

inputs = np.array(inputs)
outputs = np.array(outputs)


x_train = torch.tensor(inputs[:n_train], dtype=torch.float32).unsqueeze(-)
y_train = torch.tensor(outputs[:n_train], dtype=torch.float32).unsqueeze(-)
x_test = torch.tensor(inputs[n_train:], dtype=torch.float32).unsqueeze(-)
y_test = torch.tensor(outputs[n_train:], dtype=torch.float32).unsqueeze(-)

()
()

2. Training the FNO

# Define FNO model
model = FNO1d(
    n_modes_height=16,        # Fourier modes to keep
    hidden_channels=64,       # channel width
    in_channels=1,            # input function dimension
    out_channels=1,           # output function dimension
    n_layers=4,               # number of FNO layers
)

optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-5)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.5)

# Training loop
n_epochs = 500
batch_size = 32

for epoch in range(n_epochs):
    model.train()
    perm = torch.randperm(n_train)
    total_loss = 0
    n_batches = 0

    for i in range(0, n_train, batch_size):
        idx = perm[i:i+batch_size]
        x_batch = x_train[idx]
        y_batch = y_train[idx]

        pred = model(x_batch)
        loss = torch.nn.functional.mse_loss(pred, y_batch)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        total_loss += loss.item()
        n_batches += 1

    scheduler.step()

    if (epoch + 1) % 50 == 0:
        # Test error
        model.eval()
        with torch.no_grad():
            pred_test = model(x_test)
            test_loss = torch.nn.functional.mse_loss(pred_test, y_test)
            # Relative L2 error
            rel_err = torch.mean(
                torch.norm(pred_test - y_test, dim=1) / torch.norm(y_test, dim=1)
            )
        (
              )

3. DeepONet (Branch-Trunk Architecture)

# DeepONet: separate networks for input function (branch) and query location (trunk)

class DeepONet(torch.nn.Module):
    def __init__(self, branch_input_dim, trunk_input_dim, hidden_dim=128, p=64):
        super().__init__()
        # Branch net: processes input function (sampled at fixed sensors)
        self.branch = torch.nn.Sequential(
            torch.nn.Linear(branch_input_dim, hidden_dim),
            torch.nn.Tanh(),
            torch.nn.Linear(hidden_dim, hidden_dim),
            torch.nn.Tanh(),
            torch.nn.Linear(hidden_dim, p),
        )
        # Trunk net: processes query location
        self.trunk = torch.nn.Sequential(
            torch.nn.Linear(trunk_input_dim, hidden_dim),
            torch.nn.Tanh(),
            torch.nn.Linear(hidden_dim, hidden_dim),
            torch.nn.Tanh(),
            torch.nn.Linear(hidden_dim, p),
        )
        self.bias = torch.nn.Parameter(torch.zeros(1))

    def forward(self, u_input, x_query):
        """
        u_input: (batch, n_sensors) — input function values at sensor locations
        x_query: (batch, n_query, dim) — query locations
        Returns: (batch, n_query) — predicted output function values
        """
        b = self.branch(u_input)  # (batch, p)
        t = self.trunk(x_query)   # (batch, n_query, p)
        # Dot product + bias
        out = torch.einsum('bp,bqp->bq', b, t) + self.bias
        return out

# Usage:
# n_sensors = 100 (fixed sensor locations for input function)
# model = DeepONet(branch_input_dim=100, trunk_input_dim=1)
# pred = model(u_sensors, x_query)  # u_sensors: (B, 100), x_query: (B, N, 1)

4. Evaluation and Visualization

model.eval()
with torch.no_grad():
    pred = model(x_test)

# Plot 3 random test examples
fig, axes = plt.subplots(1, 3, figsize=(15, 4))
for i, ax in enumerate(axes):
    idx = np.random.randint(n_test)
    ax.plot(x, x_test[idx, :, 0].numpy(), 'b-', label='Input IC')
    ax.plot(x, y_test[idx, :, 0].numpy(), 'k-', linewidth=2, label='True')
    ax.plot(x, pred[idx, :, 0].numpy(), 'r--', linewidth=2, label='FNO')
    ax.set_xlabel('x')
    ax.set_ylabel('u')
    ax.legend(fontsize=9)
    ax.grid(True, alpha=0.3)
    rel = torch.norm(pred[idx] - y_test[idx]) / torch.norm(y_test[idx])
    ax.set_title(f'Test {idx}: rel. error = {rel:.3f}')

plt.suptitle('FNO: Burgers Equation', fontsize=14)
plt.tight_layout()
plt.savefig('fno_predictions.png', dpi=150, bbox_inches='tight')

Architecture Selection Guide

Architecture	Best For	Input	Limitations
FNO	Regular grids, periodic BCs	Full field on grid	Fixed resolution, periodic
DeepONet	Irregular data, different resolutions	Function at sensors + query points	Needs sensor placement
GNO (Graph NO)	Unstructured meshes, complex geometry	Graph-structured data	More complex implementation

Key Hyperparameters (FNO)

Parameter	Typical Range	Effect
n_modes	8-32	Fourier modes kept (frequency resolution)
hidden_channels	32-128	Network width
n_layers	4-6	Network depth
Learning rate	1e-3 to 1e-4	Standard Adam
Batch size	16-64	Larger is more stable
Training samples	500-5000	More data = better generalization

Tips

1. Generate training data using traditional solvers (finite differences, spectral, FEM)
2. Normalize inputs and outputs to zero mean, unit variance
3. Start with FNO for regular grids — it's the simplest and most robust
4. Use relative L2 error as the metric, not MSE (scale-invariant)
5. Test on out-of-distribution inputs to check generalization limits

Troubleshooting

Symptom	Fix
Training loss doesn't decrease	Reduce LR, increase network size, check data loading
Good train, bad test error	Overfitting — add weight decay, reduce model size, get more data
Predictions are smooth but wrong	Too few Fourier modes — increase n_modes
GPU out of memory	Reduce batch size or hidden_channels
Resolution mismatch train/test	FNO supports different resolutions if trained properly