delierium

Differential Equations' LIE symmetries Research InstrUMent

Lie point symmetries of ordinary and partial differential equations with Python and SymPy, using Janet bases.

Release 1.0.0

Had a hard time debugging and profiling (a Janet base is, after all, a Gröbner base, with all its implications during computation), and claude was of great help to find and fix all the next-to-last bugs.

delierium computes

• the determining equations of the Lie point symmetries of an ODE, a system of ODEs or a scalar PDE,
• Janet bases of linear systems of PDEs, in particular of these determining equations, fraction free, with the conditions they assume (factors assumed nonzero, special cases of the parameters),
• from a Janet basis its rank (the dimension of the solution space, for determining equations the dimension of the Lie algebra of point symmetries), its parametric and principal derivatives and its type,
• pictures: the staircase of a Janet basis, symmetry generators as vector fields with solution curves, their flows as animations.

A catalogue of about 260 equations with known symmetries from the literature checks it (see Tests). Solving the determining equations for the infinitesimals themselves is not part of this release.

Release notes

1.0.2

• Every Janet basis computes in a coefficient field of its own (coefficients.fresh_field) instead of one field shared by the whole process, which grew with every computation (#30). The symmetry catalogue runs about 20 % faster (90 s to 70 s in one process), results are unchanged.
• Internal change: the code is clean under pylint and mypy, which are now blocking in CI (#8). No change in behaviour.
• Internal change: every function has type hints, and mypy requires them for new code.
• Internal change: a version tag publishes the release to PyPI from GitHub Actions, by trusted publishing instead of an API token (#7).
• delierium.higher_infinitesimals (and its command line tool) removed: a second, limited implementation (one function of x and t, order up to 3) of what overdetermined_system_pde does for any scalar PDE.

1.0.1

• import delierium no longer replaces SymPy's Derivative.__new__ with a trimmed copy. The patch changed diff for all SymPy code in the process (no sign normalisation, no canonical form) and depended on SymPy internals. delierium now differentiates with plain SymPy, with simplify=False where it matters. Results are unchanged; the symmetry catalogue runs about 10 % slower, mostly on PDEs.
• New catalogue entry Kamke 1.535, 8 x y'^3 - 12 y y'^2 + 9 y = 0: cubic in y', so unlike y' = h(x, y) it has a finite symmetry algebra, of dimension 3. Besides x d/dx + y d/dy the generators are algebraic: with u = 3 x ± sqrt(9 x^2 - 4 y^2), u^(2/3) d/dx + 3 y u^(-1/3) d/dy, valid for x > 0 between the singular solutions y = ±3 x/2. The dimension was checked independently of the Janet basis.
• kamke.ipynb removed from the top directory (#2): an early notebook, superseded by notebooks/Catalogue_template.ipynb.

Installation

pip install delierium            # the package, needs Python 3.12 or newer
pip install "delierium[plot]"    # with matplotlib, for delierium.visualization

delierium runs on Python 3.12, 3.13 and 3.14; the whole test suite passes on each of them. With uv, choose the version with --python, e.g. uv sync --python 3.12 for the environment of the project, or, without touching it,

uv run --isolated --python 3.13 --group test pytest

From the sources, with uv:

git clone https://github.com/tapir442/delierium.git
cd delierium
uv sync                          # the package and the development tools
uv sync --group notebooks        # also JupyterLab and matplotlib, for the notebooks

How to use

The determining equations of an ODE

The Blasius equation y''' + y y'' = 0. lie_derivative_printer writes them in the notation of Lie: X_xy is the second derivative of the infinitesimal X by x and y.

>>> from sympy import Function, Symbol, diff
>>> from delierium import lie_derivative_printer, overdetermined_system_ode
>>> x = Symbol("x")
>>> y = Function("y")(x)
>>> ode = diff(y, x, 3) + y * diff(y, x, 2)
>>> determining = overdetermined_system_ode(ode, [y], [x])
>>> for e in lie_derivative_printer(determining, [y], [x], output="text"):
...     print(e)
Y_xx*y + Y_xxx
-9*X_xy + X_y*y + 3*Y_yy
-3*X_xyy - X_yy*y + Y_yyy
-X_xx*y - X_xxx + 3*Y_xxy + 2*Y_xy*y
-3*X_xxy - 2*X_xy*y + 3*Y_xyy + Y_yy*y
X_x*y - 3*X_xx + Y + 3*Y_xy
X_y
X_yy
X_yyy

In JupyterLab, lie_derivative_printer without output renders them as formulas, and ltf does so for a single expression. overdetermined_system_odes and overdetermined_system_pde work the same way for systems of ODEs and for scalar PDEs.

Their Janet basis

>>> from delierium import janet_basis_from_ode
>>> for e in janet_basis_from_ode(ode, y, x):
...     print(e)
D(X(y(x), x), x) + (1/y(x)) * Y(y(x), x)
D(X(y(x), x), y(x))
D(Y(y(x), x), x)
D(Y(y(x), x), y(x)) + (-1/y(x)) * Y(y(x), x)

Every derivative of X and Y is determined by these four equations, only the values of X and Y themselves are free: the Blasius equation has a two-dimensional Lie algebra of point symmetries, d/dx and x d/dx - y d/dy.

Janet bases of linear systems

JanetBasis takes a list of linear homogeneous PDEs, the unknown functions and the variables (highest first), and optionally a ranking (Mgrevlex, the default, Mgrlex, Mlex). Schwarz's system (2.25):

>>> from sympy import symbols
>>> from delierium import JanetBasis
>>> x, y = symbols("x y")
>>> z = Function("z")(x, y)
>>> w = Function("w")(x, y)
>>> g1 = diff(z, y, y) + diff(z, y) / (2 * y)
>>> g2 = diff(w, x, x) + 4 * diff(w, y) * y**2 - 8 * y**2 * diff(z, x) - 8 * w * y
>>> g3 = diff(w, x, y) - diff(z, x, x) / 2 - diff(w, x) / (2 * y) - 6 * y**2 * diff(z, y)
>>> g4 = diff(w, y, y) - 2 * diff(z, x, y) - diff(w, y) / (2 * y) + w / (2 * y**2)
>>> janet = JanetBasis([g2, g3, g4, g1], (w, z), (x, y))
>>> for e in janet.S:
...     print(e)
D(z(x, y), y)
D(z(x, y), x) + (1/(2*y)) * w(x, y)
D(w(x, y), y) + (-1/y) * w(x, y)
D(w(x, y), x)
>>> janet.rank()
2
>>> print(janet.parametric_derivatives())
[w(x, y), z(x, y)]
>>> print(janet.principal_derivatives(1))
[Derivative(w(x, y), x), Derivative(w(x, y), y), Derivative(z(x, y), x), Derivative(z(x, y), y)]

rank() is oo if there are infinitely many parametric derivatives. assumed_nonzero() lists the factors the computation divided by: where one of them vanishes, for special values of parameters or on singular lines, the Janet basis may differ. parameter_conditions() gives these special cases of the parameters.

Pictures

delierium.visualization needs matplotlib (delierium[plot]) and is not imported by import delierium:

from delierium.visualization import staircase, vector_fields, ode_solutions, animate_flow

staircase(janet)                                  # leading, principal, parametric derivatives
curves = ode_solutions(ode, y)                    # solution curves of an ODE
vector_fields([("1", "0"), ("x", "-y")], symbols("x y"), curves)
animate_flow(("x", "-y"), symbols("x y"), curves) # in Jupyter: HTML(_.to_jshtml())

A generator is a tuple of its components, the independent variables first: ("x", "-y") is x d/dx - y d/dy.

The package

The public interface is what delierium exports; help(delierium) lists it:

• determining equations: overdetermined_system_ode, overdetermined_system_odes, overdetermined_system_pde, prolongation, make_infinitesimal, create_infinitesimals
• Janet bases: JanetBasis (with rank, parametric_derivatives, principal_derivatives, type, assumed_nonzero, parameter_conditions, representation), janet_basis_from_ode, janet_basis_from_odes, integrability_conditions, and checks against published bases: is_janet_basis_of, is_janet_basis_of_ode, is_janet_basis_of_odes
• rankings: Context, Mgrevlex, Mgrlex, Mlex
• the notation of Lie: lie_form, ltf, lie_derivative_printer
• operators: euler_operator, frechet_derivative, adjoint_frechet_derivative, variational_derivative
• pictures: the module delierium.visualization

Tests and the symmetry catalogue

tests/symmetry_catalog.py is a catalogue of about 260 ODEs, systems of ODEs and PDEs with their known symmetries: Kamke's collection (as classified in Schwarz's Appendix E), the examples and exercises of Arrigo, Schwarz, Hydon, Baumann and others, and classical equations. For each, the listed generators have to solve the determining equations and the rank of their Janet basis has to be the dimension of the symmetry algebra. tests/test_schwarz_examples.py checks the Janet bases printed in Schwarz's book, coefficients included.

uv run pytest                    # the fast tests, including the doctests of this README
uv run pytest -m slow -n auto    # the whole catalogue, about 15 s

Notebooks

• notebooks/Catalogue_template.ipynb: pick an equation of the catalogue by name; its Janet basis and pictures.
• notebooks/Arrigo, Baumann, Schwarz, Khare-Timol: the examples of these books and papers, in their order, built the same way.
• notebooks/Backlund: Bäcklund transformations (sine-Gordon, Liouville, Burgers, KdV), and the symmetries of their equations.

Open problems

Equations delierium cannot handle yet are tests in tests/test_open_problems.py, each documented in its module docstring: what is asked, what delierium does, why it fails, what is known, and ways forward. They are skipped by default; run them with

uv run pytest -m too_slow --run-too-slow

1. The symmetries of 2 (K(u) u')' + x u' = 0 for an arbitrary K(u) (the Boltzmann-reduced nonlinear diffusion equation, a group classification problem): the Janet basis does not finish, as every differentiation brings in the next derivative of K as a new generator of the coefficients.
2. The determining equation of the general second order ODE u'' = F(x, u, u') with an arbitrary F: splitting the symmetry condition fails, as F is an arbitrary function of the jet variable u'.
3. Kamke 6.171 and 6.219 with symbolic parameters: the Janet basis does not finish in reasonable time. The catalogue checks them at random parameter values, which give the generic dimension.

Literature

• Werner M. Seiler: Involution. The Formal Theory of Differential Equations and its Applications in Computer Algebra, Springer Berlin 2010, ISBN 978-3-642-26135-0.
• Fritz Schwarz: Algorithmic Lie Theory for Solving Ordinary Differential Equations, CRC Press 2008, ISBN 978-1-58488-889-5.
• Fritz Schwarz: Loewy Decomposition of Linear Differential Equations, Springer Wien 2012, ISBN 978-3-7091-1687-6.
• Daniel J. Arrigo: Symmetry Analysis of Differential Equations, Wiley Hoboken/New Jersey 2015, ISBN 978-1-118-72140-7.
• Gerd Baumann: Symmetry Analysis of Differential Equations with Mathematica, Springer New York Berlin Heidelberg 2000, ISBN 0-387-98552-2.
• Peter E. Hydon: Symmetry Methods for Differential Equations, Cambridge University Press 2000.
• Erich Kamke: Differentialgleichungen. Lösungsmethoden und Lösungen.
• John Starrett: Solving Differential Equations by Symmetry Groups, https://www.researchgate.net/publication/233653257_Solving_Differential_Equations_by_Symmetry_Groups
• Alexey A. Kasatkin, Aliya A. Gainetdinova: Symbolic and Numerical Methods for Searching Symmetries of Ordinary Differential Equations with a Small Parameter and Reducing Its Order, https://link.springer.com/chapter/10.1007%2F978-3-030-26831-2_19
• Vishwas Khare, M. G. Timol: New Algorithm In SageMath To Check Symmetry Of Ode Of First Order, https://www.researchgate.net/publication/338388495_New_Algorithm_In_SageMath_To_Check_Symmetry_Of_Ode_Of_First_Order

License

MIT, see LICENSE.