foundax 0.1.6

One-stop JAX foundation model repository

foundax

Unified JAX model zoo for operator learning, PDE surrogates, and Equinox foundation-model wrappers.

uv pip install foundax

Overview

foundax provides two main model groups:

• Core Equinox architectures in foundax/architectures/ (FNO, UNet, DeepONet, GNOT family, and others)
• Equinox wrappers for larger vendored model families (Poseidon, MORPH, MPP, Walrus, BCAT, PDEformer-2, DPOT, PROSE)

Quick Start

import foundax as fx

# Core models
model = fx.mlp(in_features=2, output_dim=1, hidden_dims=64, num_layers=3)
model = fx.fno2d(in_features=1, hidden_channels=32, n_modes=16)
model = fx.unet2d(in_channels=1, out_channels=1)
model = fx.deeponet(branch_type="mlp", trunk_type="mlp")

# Foundation wrappers (namespace style)
model = fx.poseidon.T()   # T/B/L
model = fx.morph.S()      # Ti/S/M/L
model = fx.mpp.B(n_states=12)  # Ti/S/B/L
model = fx.walrus.base()
model = fx.bcat.base()
model = fx.pdeformer2.small()  # small/base/fast
model = fx.dpot.Ti()      # Ti/S/M/L/H
model, variables = fx.prose.fd_1to1()

Integration With jNO

import foundax as fx
import jno

net = jno.nn.wrap(fx.mlp(in_features=2, output_dim=1))
net.optimizer(optax.adam, lr=1e-3)

Notes

• Top-level convenience aliases are still available (for example fx.poseidonT()), but namespace-style access is recommended for readability.
• Foundation-model wrappers are documented in detail in docs/equinox-architectures.md.

License

This project is licensed under the MIT License.

Foundation models remain subject to their original licenses. See THIRD_PARTY_LICENSES for details. Some pretrained weights (for example Poseidon) are released under non-commercial terms.