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jNO (jax Numerical Operators) is a JAX-native library for differentiable numerical methods. Classical solvers — finite elements, finite differences, and spectral (RCWA) — and scientific machine learning — PINNs, neural operators, Bayesian inference — are two pillars on one substrate: everything (a weak form, a strong-form stencil, a PDE residual, a supervised loss, a sensitivity diagnostic) is written in one symbolic tracing language and compiled into a single GPU-ready, end-to-end differentiable graph. Because every solve is differentiable, a forward solve, an inverse problem, and a trained network compose instead of living in separate frameworks.

Get started

  • Install

    Pip, Pixi, or Docker — under 30 seconds. GPU support is on by default.

  • 30-min Quickstart

    A 2-D Poisson problem with random diffusion, trained end-to-end. Teaches the full pipeline.

  • Tutorials

    29 worked examples across elliptic, parabolic, hyperbolic, coupled, inverse, integral, stochastic, FEM, and Bayesian problems.

  • API Reference

    Auto-generated reference for jno.core, jno.domain, jno.nn, jno.np, and jno.fn.

Capability matrix

What works today, what is experimental, what is not yet supported.

Each status badge links to the tutorial (or, where no dedicated tutorial exists, the closest reference page) that demonstrates the capability.

Capability Status Notes
Forward PINNs (residual minimisation) ✅ stable Hard or soft BC enforcement
Operator learning (DeepONet, FNO, U-Net, PROSE via foundax) ✅ stable PDE-residual or data-driven; FieldView adds FD physics on grid outputs
Inverse problems (parameter recovery, surrogate inversion) ✅ stable See Inverse Problems
FEM / Variational PINNs ✅ stable Nodal TRI3 / TRI6 / QUAD4 weak-form assembly; experimental Raviart–Thomas / Nédélec edge elements (H(div)/H(curl), 2-D) — see known limitations
Adaptive resampling (RAD, RARD, CR3, R3, pinnfluence) ✅ stable See Adaptive Resampling
Stochastic PDEs and noise nodes (gaussian / uniform / laplace) ✅ stable Fokker–Planck, stochastic forcing
Bayesian PINNs (NUTS, HMC, MALA, SGLD, SGHMC, VI) ✅ stable see Bayesian Sampling
Parameter-efficient fine-tuning (LoRA, DoRA, rsLoRA, PiSSA, VeRA, LoKr, OFT, IA3) ✅ stable Chain .lora(...) on any wrapped model
Training explainability (gradient conflict, NTK, Hessian, loss landscape, input sensitivity, residual stats) ✅ stable See Explainability
Foundation-model integration (foundax MLPs, transformers, DeepONet, FNO, PROSE) ✅ stable Wrap any Equinox module via jno.nn(...)
Hybrid data + model parallelism ✅ stable core(mesh=(batch, model))
W&B logging + Orbax checkpointing ✅ stable
IREE / MLIR compiled inference for deployment ✅ stable
Hyperparameter / architecture search 🟡 beta Grid + Nevergrad
Multi-physics coupling ✅ stable Rayleigh–Bénard, Boussinesq, poroelastic — all via jno.fem

Common terminology

A few jNO-specific terms appear throughout the docs. See the Glossary page for full definitions:

  • Trace / tracing system — the unified symbolic graph that holds domain points, network calls, residuals, and losses.
  • Placeholder — any symbolic node in that graph (a Variable, a network call, an operator).
  • Constraint — a single optimisable expression passed to jno.core([…]). Typically pde.mse, but can be any scalar.
  • Model controls — fine-grained per-parameter knobs (freeze, LoRA, initialisation, dtype) configured on a wrapped network.
  • Mesh — overloaded: a spatial mesh for the PDE domain and a device mesh (batch, model) for parallelism.

Tutorials

Roughly fifty worked examples, ordered from simplest to most involved. Start at 01 if you are new; jump into 05 (inverse), 08 (FEM), or 10 (Bayesian) if you have a specific goal.

Group Topic
01 Basics 1-D Laplace, Poisson, Biharmonic
02 Elliptic Anisotropic / variable-coefficient Poisson, Helmholtz, mixed BC
03 Parabolic Heat 1-D/2-D, Reaction-Diffusion, Allen–Cahn
04 Hyperbolic Advection–Diffusion, Burgers, Telegraph, Wave
05 Coupled & Inverse Coupled systems, parameter recovery, surrogate inversion, HyCo
06 Integration Boundary flux, divergence theorem, Fredholm, integro-differential
07 Stochastic Fokker–Planck, stochastic forcing
08 FEM & Variational PINNs Poisson 2-D, Allen–Cahn, Robin BC, Helmholtz 3-D
09 W&B logging Reproducible training runs with Weights & Biases
10 Bayesian PINNs NUTS / HMC chains, VI, inverse problems, BNN regression, warm-start initialisations (Pathfinder, Laplace, SVGD)

The full nav is in the left sidebar; the source scripts live under docs/tutorial_examples/.

Cite jNO

Citing helps us justify continued development. See the Cite this repository button on GitHub (reads CITATION.cff) or copy the BibTeX entry from the README.