PINNx: Physics-Informed Neural Networks for Scientific Machine Learning in JAX
PINNx is a library for scientific machine learning and physics-informed learning in JAX.
It is inspired from DeepXDE but is enhanced by our
Brain Dynamics Programming (BDP) ecosystem.
For example, it leverages
- brainstate for just-in-time compilation,
- brainunit for dimensional analysis,
- braintools for checkpointing, loss functions, and other utilities.
Quickstart
Define a PINN with explicit variables and physical units.
import brainstate as bst
import brainunit as u
import pinnx
# geometry
geometry = pinnx.geometry.GeometryXTime(
geometry=pinnx.geometry.Interval(-1, 1.),
timedomain=pinnx.geometry.TimeDomain(0, 0.99)
).to_dict_point(x=u.meter, t=u.second)
uy = u.meter / u.second
v = 0.01 / u.math.pi * u.meter ** 2 / u.second
# boundary conditions
bc = pinnx.icbc.DirichletBC(lambda x: {'y': 0. * uy})
ic = pinnx.icbc.IC(lambda x: {'y': -u.math.sin(u.math.pi * x['x'] / u.meter) * uy})
# PDE equation
def pde(x, y):
jacobian = approximator.jacobian(x)
hessian = approximator.hessian(x)
dy_x = jacobian['y']['x']
dy_t = jacobian['y']['t']
dy_xx = hessian['y']['x']['x']
residual = dy_t + y['y'] * dy_x - v * dy_xx
return residual
# neural network
approximator = pinnx.nn.Model(
pinnx.nn.DictToArray(x=u.meter, t=u.second),
pinnx.nn.FNN(
[geometry.dim] + [20] * 3 + [1],
"tanh",
bst.init.KaimingUniform()
),
pinnx.nn.ArrayToDict(y=uy)
)
# problem
problem = pinnx.problem.TimePDE(
geometry,
pde,
[bc, ic],
approximator,
num_domain=2540,
num_boundary=80,
num_initial=160,
)
# training
trainer = pinnx.Trainer(problem)
trainer.compile(bst.optim.Adam(1e-3)).train(iterations=15000)
trainer.compile(bst.optim.LBFGS(1e-3)).train(2000, display_every=500)
trainer.saveplot(issave=True, isplot=True)
Installation
- Install the stable version with
pip:
pip install pinnx --upgrade
Documentation
The official documentation is hosted on Read the Docs: https://pinnx.readthedocs.io/
See also the BDP ecosystem
We are building the Brain Dynamics Programming ecosystem: https://ecosystem-for-brain-dynamics.readthedocs.io/
Metadata
Release files for pinnx 0.0.2.post20241220
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pinnx-0.0.2.post20241220-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details |
Release files / pinnx-0.0.2.post20241220-py2.py3-none-any.whl
| Download URL | pinnx-0.0.2.post20241220-py2.py3-none-any.whl |
|---|---|
| Size | 124.1 kB |
| Tags | Python 2 Python 3 |
|
SHA-256 checksum How to use checksums |
c813301ca3319273ca9ae0c88724f564a1e13111b3a554c3d053d97299ac4d2c
|
|
BLAKE2b-256 checksum How to use checksums |
74686c6787ad3d6a01772d59fe905aa2a73441679bb817e25f937d92ae1202f6
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.11.5
|
Security for pinnx 0.0.2.post20241220
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.