moju 1.1.0

Physics supervision and audit tools for SciML models, with JAX-native residuals and PyTorch support.

moju

Physics supervision and audit tools for SciML and Physics AI.

pip install moju

Paper and demo notebooks (Colab)

Notebook	Description
Path A — arXiv reproduction	Train 32×32×32 PINN, Moju audit, verify vs paper reference
Path B — instant audit	Load pre-exported w2 states, audit and visualize (no training)

Open-in-Colab badges require the notebooks on GitHub main. See examples/Notebooks/README.md.

Moju helps you turn predicted state fields into governing-law residuals, physics losses, constitutive consistency checks, and audit reports. It is JAX-native at the core, with a PyTorch-facing interface available through moju.torch.

What Moju Does

• Builds residuals from composable Laws, Groups, and Models.
• Turns governing-law residuals into a differentiable training loss with build_loss.
• Audits predictions with per-key, per-category, and overall admissibility scores.
• Infers law-linked constitutive checks where the governing equation implies a material property.
• Visualizes training/eval diagnostics, spatial residuals, and constitutive divergence and consistency.

Moju is not a training framework or a solver. It is a physics supervision layer you can use with PINNs, CFD surrogates, neural operators, digital twins, or any workflow that can provide a state_pred dictionary.

5-Minute Example: 1D Slab Cooling

This is the minimal Path B flow: pass state_pred directly. The derivatives are already in the state, so no finite-difference inference is needed. The Fourier law automatically adds the law-linked thermal_diffusivity implied audit.

import jax.numpy as jnp

from moju.monitor import audit, build_loss, build_minimal_residual_engine, visualize

L = 0.02
rho = 2700.0
cp = 900.0
k = 200.0
alpha = k / (rho * cp)

x = jnp.linspace(0.0, L, 64)
t = jnp.ones_like(x) * 10.0

# A toy variable temperature profile with supplied derivatives.
T = 300.0 + 20.0 * (1.0 - x / L) ** 2
T_laplacian = jnp.ones_like(x) * (40.0 / (L**2))
T_t = alpha * T_laplacian

state_pred = {
    "x": x,
    "t": t,
    "T": T,
    "T_t": T_t,
    "T_laplacian": T_laplacian,
    "L": jnp.ones_like(x) * L,
    "k": jnp.ones_like(x) * k,
    "rho": jnp.ones_like(x) * rho,
    "cp": jnp.ones_like(x) * cp,
    "alpha": jnp.ones_like(x) * alpha,
}

engine = build_minimal_residual_engine(
    law_names=["fourier_conduction"],
    coord_dimension=1,
)

residuals = engine.compute_residuals(state_pred, run_mode="training")
loss = build_loss(residuals)
report = audit(engine.log)
fig = visualize(engine.log, engine=engine)

print("Physics loss:", float(loss))
print("Overall admissibility:", report["overall_admissibility_score"])
print("Categories:", report["per_category"].keys())
fig.show()

What happens here:

• build_minimal_residual_engine(...) creates the Fourier conduction law and the needed fo group row.
• state_pred supplies the variable field T, its derivatives T_t and T_laplacian, coordinates, and material properties.
• law_implied_audits=True is the default, so Moju adds constitutive/thermal_diffusivity/law_fourier_conduction/implied_delta.
• build_loss uses governing-law residuals for training.
• audit and visualize use the log plus engine.last_residuals to report physics diagnostics.

Core Concepts

• moju.piratio.Models - constitutive relationships such as viscosity, density, diffusivity, wave speed, and turbulence closures.
• moju.piratio.Groups - dimensionless quantities such as re, pr, pe, fo, ma, and bi, materialized into state for laws.
• moju.piratio.Laws - governing-equation residuals for heat, diffusion, wave, momentum, mass, Darcy/Brinkman, Poisson, Burgers, and related equations.
• moju.piratio.Operators - JAX autodiff helpers such as gradients, divergence, Laplacian, curl, and time derivatives.
• moju.monitor.ResidualEngine - runs laws, groups, constitutive audits, optional data comparisons, and records audit logs.
• moju.monitor.audit - converts logs into R_norm, admissibility scores, category summaries, and report data.
• moju.monitor.visualize - Plotly dashboards for training/eval residuals, category scores, spatial fields, and constitutive diagnostics. The constitutive row shows a Divergence heatmap (normalised as (model − implied) / (|model| + ε)) alongside a Constitutive Consistency line plot with spatially varying ±0.1 % / ±0.5 % / ±1 % acceptability bands and tier boundary markers centred on the model prediction.

Training vs Eval

compute_residuals(..., run_mode="training") is the default for optimization loops. It runs laws, groups, and constitutive implied audits. state_ref is ignored in training mode.

Use run_mode="eval" when you want reference comparisons. In eval mode, state_ref enables constitutive ref_delta and data/ residuals.

Overall admissibility is the minimum of the finite category scores participating in the current run mode. Training rolls up laws and constitutive categories. Eval also includes data when present. Legacy logs may still contain a historical scaling category.

Why two admissibility metrics?

Governing laws are scored with RMS R_eff (average compliance across collocation points). Constitutive implied_delta / ref_delta closure keys use worst-point max |δ| for admissibility — a PINN can satisfy the PDE on average while cheating closure at a few hotspots. Logged rms and build_loss stay RMS everywhere; audit/reporting uses the split. Constitutive category rolls up with minimum (not geometric mean). See Admissibility metrics in docs/monitor_training_vs_eval.md.

For multi-scale or high-Re problems, governing laws use auto term-balance scale_k by default (floored at ≈ 1e-2). Use law_scale_mode="fixed" or per-key audit(..., r_ref={...}) when you want a fixed gauge — see Calibrating scale_k in that doc. Path B SI uploads: state_units="dimensional" (Studio checkbox State in physical units (SI)).

Details: docs/monitor_training_vs_eval.md.

PyTorch Support

Install the PyTorch extra:

pip install "moju[torch]"

moju.torch provides:

• TorchResidualEngine - PyTorch-facing residual engine with parity-oriented behavior.
• build_loss_torch and r_eff_scalar_torch - Torch-native R_eff loss helpers.
• wrap_law_torch - wrap JAX Laws.* functions for use with Torch tensors through jax2torch.
• Torch-native nondimensionalization helpers.

Start with scripts/torch_laws_jax2torch_example.py. The implementation is covered by tests/test_torch_engine.py and tests/test_torch_interop.py.

Installation profiles

Install	Use when
pip install moju	Default: residuals, audit, Plotly visualize(), PDF export
pip install "moju[io]"	Science file I/O for state_ref loaders (xarray, HDF5, VTK, NetCDF, …)
pip install "moju[studio]"	Moju Studio Streamlit app (+ HDF5/NetCDF uploads)
pip install "moju[torch,io]"	PyTorch training + file loaders
pip install "moju[dev]"	Development (pytest, black, ruff)

Training demos that use optax (e.g. examples/slab_cooling_demo.py) require pip install optax in addition to moju.

Documentation

• GitHub Pages source and API overview: docs/
• Training vs eval behavior: docs/monitor_training_vs_eval.md
• Law-linked constitutive implied audits: docs/law_implied_audits.md
• Moju Studio: apps/moju_studio/README.md
• Versioning policy: VERSIONING.md
• Changelog: CHANGELOG.md

Examples

• Full 1D slab cooling demo: examples/slab_cooling_demo.py
• CFD snapshot audit: examples/cfd_snapshot_cookbook_heat_1d.py
• Path B finite-difference law fill: examples/cookbook_path_b_fd_law_laplace.py
• Constitutive divergence dashboard: examples/cookbook_constitutive_divergence.py
• Torch interop: scripts/torch_laws_jax2torch_example.py

Philosophy

Moju does not define physics for you. It gives you a structured way to apply the physics you already trust, measure residuals consistently, and surface where a model agrees or disagrees with governing laws and constitutive assumptions.

License

MIT License. Developed by Ifimo Lab, a division of Ifimo Analytics.