uPDE: A Python library for solving systems of nonlinear partial differential equations

uPDE is a user-centric Python library for solving systems of nonlinear, coupled partial differential equations, both steady and unsteady. Its central design principle is a physics-focused declarative API in which users construct a PDE term by term—adding diffusion, advection, sources, and fluxes individually—so that the code structure mirrors the governing equations directly. This encourages physical understanding and gives users explicit control over every term in their model, while delegating all numerical machinery—spatial discretization, time integration, and steady-state solving—to well-tested solvers mainly from scipy . The user describes the equation in a physics-focused manner: which terms are present, what the coefficients are, and what the boundary and initial conditions are. Everything else—assembling the RHS, flattening multi-field state vectors, choosing ghost-cell values at non-periodic boundaries—happens automatically. uPDE currently handles 1D and 2D problems, nonlinear field-dependent coefficients, Dirichlet, Neumann, and per-axis periodic boundary conditions, and tightly coupled multi-field systems. We expect it to be useful both as a research prototyping tool and as a teaching aid for courses on numerical methods, transport phenomena, and other subjects involving PDE-based modelling.

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Publication Details

Journal
SoftwareX
Published
2026-05-11
DOI
https://doi.org/10.1016/j.softx.2026.102715
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

uPDE: A Python library for solving systems of nonlinear partial differential equations

Tony Saad
SoftwareX
Model Reduction and Neural Networks
article

uPDE: A Python library for solving systems of nonlinear partial differential equations

Tony Saad
article en

Abstract

uPDE is a user-centric Python library for solving systems of nonlinear, coupled partial differential equations, both steady and unsteady. Its central design principle is a physics-focused declarative API in which users construct a PDE term by term—adding diffusion, advection, sources, and fluxes individually—so that the code structure mirrors the governing equations directly. This encourages physical understanding and gives users explicit control over every term in their model, while delegating all numerical machinery—spatial discretization, time integration, and steady-state solving—to well-tested solvers mainly from scipy . The user describes the equation in a physics-focused manner: which terms are present, what the coefficients are, and what the boundary and initial conditions are. Everything else—assembling the RHS, flattening multi-field state vectors, choosing ghost-cell values at non-periodic boundaries—happens automatically. uPDE currently handles 1D and 2D problems, nonlinear field-dependent coefficients, Dirichlet, Neumann, and per-axis periodic boundary conditions, and tightly coupled multi-field systems. We expect it to be useful both as a research prototyping tool and as a teaching aid for courses on numerical methods, transport phenomena, and other subjects involving PDE-based modelling.

SoftwareXVol. 34
University of Utah (US)
Directorate for Engineering
Openalex Percentile: Top 6%
Model Reduction and Neural Networks
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uPDE: A Python library for solving systems of nonlinear partial differential equations — Tony Saad · SoftwareX (2026) | TGRS Research Map | TGRS