I'm stuck! How to efficiently debug computational solid mechanics models so you can enjoy the beauty of simulations
Ester Comellas, Jean-Paul Pelteret, Wolfgang Bangerth

TL;DR
This paper offers practical strategies and a structured approach for debugging computational solid mechanics models, aiming to help newcomers identify and fix errors efficiently to enjoy the modeling process.
Contribution
It provides a comprehensive perspective on error sources, symptom-based troubleshooting strategies, and methods to maintain bug-free models over time in computational solid mechanics.
Findings
Symptom-based error identification improves debugging efficiency.
Catalogue of common error sources aids in quick diagnosis.
Strategies for maintaining bug-free models over ongoing development.
Abstract
A substantial fraction of the time that computational modellers dedicate to developing their models is actually spent trouble-shooting and debugging their code. However, how this process unfolds is seldom spoken about, maybe because it is hard to articulate as it relies mostly on the mental catalogues we have built with the experience of past failures. To help newcomers to the field of material modelling, here we attempt to fill this gap and provide a perspective on how to identify and fix mistakes in computational solid mechanics models. To this aim, we describe the components that make up such a model and then identify possible sources of errors. In practice, finding mistakes is often better done by considering the symptoms of what is going wrong. As a consequence, we provide strategies to narrow down where in the model the problem may be, based on observation and a catalogue of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsModel-Driven Software Engineering Techniques · Simulation Techniques and Applications · Scientific Computing and Data Management
