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arxiv: 1906.10799 · v1 · pith:YWXQR4DXnew · submitted 2019-06-26 · 📡 eess.SY · cs.SY

Computer-aided modelling of complex physical systems with BondGraphTools

Peter Cudmore , Peter J. Gawthrop , Michael Pan , Edmund J. Crampin This is my paper

Pith reviewed 2026-05-25 16:09 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords bond graphspython librarymulti-physics modelingsystems biologyoptomechanicssymbolic compositionmodeling toolsscientific python
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The pith

BondGraphTools is a Python library for composing multi-physics models symbolically via the bond graph method.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents BondGraphTools as a new library that lets users build and combine models of systems involving multiple physical domains by writing Python scripts. It rests on the established bond graph approach but adds a symbolic composition interface meant to fit directly into the standard scientific Python stack. An optomechanics example illustrates how the library can shorten the modeling step for complex interactions. The authors point to systems biology as a natural application area where models must cross mechanical, chemical, and electrical domains. The core goal is to make scripted, reusable model construction practical without leaving the Python ecosystem.

Core claim

BondGraphTools supplies a programming interface that performs symbolic composition of bond graph models of multi-physics systems and is built to interoperate with the existing scientific Python toolchain. The design supports scripted construction of complex models, demonstrated through an optomechanics case, and is positioned for use in cross-domain modeling tasks such as those arising in systems biology.

What carries the argument

BondGraphTools, a Python library that implements the bond graph methodology for symbolic model composition.

If this is right

  • Models can be assembled and modified by writing short Python scripts rather than manual diagram editing.
  • The same model objects remain available for further symbolic manipulation or numerical simulation inside standard Python workflows.
  • Cross-domain interactions in systems biology become easier to represent because energy ports from different physical domains connect directly.
  • Model components can be reused and composed at the code level, supporting rapid exploration of design variants.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the library is adopted, bond-graph models could become version-controlled artifacts that travel alongside analysis scripts in research repositories.
  • The symbolic composition step might later be linked to automatic code generation for real-time control or parameter estimation routines.
  • Researchers in fields outside the paper's examples, such as fluid-power systems or thermal networks, could apply the same interface once the core library is extended with additional element types.

Load-bearing premise

Users working on multi-physics problems will find the combination of bond graphs and a Python scripting interface faster or easier than the modeling tools they already use.

What would settle it

A controlled comparison in which experienced modelers build the same optomechanics or systems-biology model in BondGraphTools and in a leading alternative package and show no reduction in total time or error rate.

Figures

Figures reproduced from arXiv: 1906.10799 by Edmund J. Crampin, Michael Pan, Peter Cudmore, Peter J. Gawthrop.

Figure 3
Figure 3. Figure 3: Illustration of the set up of an optomechanical experiment. Here an optical cavity [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗
read the original abstract

BondGraphTools is a Python library for scripted modelling of complex multi-physics systems. In contrast to existing modelling solutions, BondGraphTools is based upon the well established bond graph methodology, provides a programming interface for symbolic model composition, and is intended to be used in conjunction with the existing scientific Python toolchain. Here we discuss the design, implementation and use of BondGraphTools, demonstrate how it can be used to accelerate systems modelling with an example from optomechanics, and comment on current and future applications in cross-domain modelling, particularly in systems biology.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper presents BondGraphTools, a Python library for scripted modeling of complex multi-physics systems based on the bond graph methodology. It provides a programming interface for symbolic model composition, integrates with the scientific Python toolchain, describes the design and implementation, demonstrates usage via an optomechanics example, and discusses applications in cross-domain modeling including systems biology.

Significance. If the library functions as described, it would offer an open-source Python-based tool that combines established bond-graph methods with symbolic computation and the SciPy ecosystem, potentially aiding multi-physics modeling workflows in systems biology and related fields where scripted composition is advantageous.

major comments (2)
  1. [Optomechanics example] Optomechanics example section: the demonstration shows usage but supplies no quantitative benchmarks (e.g., modeling time, model size, or accuracy metrics) or comparisons against manual bond-graph construction or alternative tools, leaving the claim of accelerating systems modeling unsupported by evidence.
  2. [Design and implementation] Design and implementation discussion: while the high-level architecture is outlined, there is insufficient detail on the symbolic composition algorithms, handling of algebraic loops, or scalability limits for large multi-domain models, which bears on the robustness of the central claim that the library enables reliable scripted modeling.
minor comments (2)
  1. [Abstract] Abstract: could explicitly note the library's open-source status and GitHub availability to strengthen the claim of integration with the existing Python toolchain.
  2. [References] References: the manuscript would benefit from citing prior Python or open-source bond-graph implementations for context on novelty.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful review and constructive suggestions. We address each major comment below and indicate where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Optomechanics example] Optomechanics example section: the demonstration shows usage but supplies no quantitative benchmarks (e.g., modeling time, model size, or accuracy metrics) or comparisons against manual bond-graph construction or alternative tools, leaving the claim of accelerating systems modeling unsupported by evidence.

    Authors: The optomechanics example is intended primarily as an illustration of the scripting interface and model composition workflow rather than a performance study. We agree that the phrasing 'accelerate systems modelling' could be interpreted as implying quantitative gains. In revision we will qualify this language to emphasize ease of model construction and integration with the SciPy ecosystem, and will add a short paragraph noting that timing measurements are available in the accompanying repository but fall outside the scope of the present paper. revision: yes

  2. Referee: [Design and implementation] Design and implementation discussion: while the high-level architecture is outlined, there is insufficient detail on the symbolic composition algorithms, handling of algebraic loops, or scalability limits for large multi-domain models, which bears on the robustness of the central claim that the library enables reliable scripted modeling.

    Authors: We will expand the 'Design and implementation' section with a concise description of the symbolic composition procedure (leveraging SymPy for equation assembly), a note on the current handling of algebraic loops via the underlying solver, and a brief discussion of observed scalability limits based on the models tested during development. These additions will be kept at a level appropriate for a tools paper while addressing the request for greater transparency. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is a descriptive announcement of a Python software library for bond-graph modeling. It presents no mathematical derivations, equations, fitted parameters, predictions, or uniqueness theorems. The central claims concern the library's existence, interface, and intended use with SciPy; these are self-contained factual statements about software and do not reduce to any input by construction. No load-bearing self-citations or ansatzes are present.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Paper is a software tool description; no free parameters, mathematical axioms, or invented physical entities are introduced.

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discussion (0)

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Reference graph

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