Computational Models and Simulations

SciViD

SciViD welcomes original computational and theoretical research, including numerical simulation, analytical modeling and machine-learning models, as well as reviews of models and methods. Video allows authors to show how a model is set up, how it behaves as parameters change, and how its output compares with theory or experiment.

Paper categories

1. Numerical and analytical modeling

Original research in which the authors’ own code or modeling software (for example MATLAB, COMSOL, ANSYS, LAMMPS or custom Python code) produces new results.

Requirements: Describe the model and the numerical method. Report verification (for example convergence and mesh or time-step independence) and validation against analytical solutions or experimental data, where applicable. State the main assumptions and the sensitivity of the results to key parameters. Visuals presented as results must be generated from the simulation data, and all post-processing and rendering steps must be described.

2. Machine-learning and predictive modeling

Original research in which a trained model predicts physical, chemical or biological behavior (for example protein structure, material properties or climate patterns).

Requirements: Describe the datasets and their provenance, the training procedure, how training and test data were separated, the validation metrics, and the comparison with baseline methods. Visuals showing the model’s predictions must be labeled “Model-Predicted Output” and kept distinct from illustrative material.

3. Reviews of models and methods

Educational or review papers that explain existing models or theoretical frameworks.

Requirements: Cite all original sources. Conceptual animations (for example made with Manim or Blender) and visual metaphors are welcome but must be labeled “Conceptual Animation” or “Illustrative Metaphor”.

Prohibited practices

Pseudo-simulations: presenting AI-generated or hand-made animations as the output of a scientific solver or trained model.
AI-altered output: applying AI upscaling, frame interpolation or generative enhancement to footage labeled as Simulation Output or Model-Predicted Output, since these create frames the model did not compute.
Unlabeled or misleading visuals: animations that are not labeled as conceptual, or that suggest behavior not supported by the data or cited theory.
Selective presentation: showing only favorable runs, parameter ranges or time windows without disclosing that others were performed.
Unattributed work: using code, datasets or visual outputs created by others without citation or permission under their license.

Submission requirements

Computational setup: software and versions, governing equations, initial and boundary conditions, numerical method and discretization, solver settings and convergence criteria.
Assumptions and limitations: the main simplifications in the model and the range of conditions in which the results are expected to hold.
Visual labeling: every segment of the video must carry an on-screen label identifying it as Simulation Output, Model-Predicted Output, Conceptual Animation or Illustrative Metaphor, as applicable. Segments labeled as output must reflect the data accurately, with color scales and units shown.
Code and data availability: a statement explaining where the code, input files and data can be obtained, or why they cannot be shared. Deposit in an archived repository with a DOI is strongly encouraged.
AI Use Statement: as specified in SciViD’s Integrity Guide, covering any AI-generated or AI-enhanced narration, visuals or animation, included in the submission metadata and the video credits.
Narrative walkthrough: the video should explain the purpose of the model, the choice of parameters, and the significance and limitations of the results.

Interactive virtual labs

An interactive lab lets readers change parameters and watch a model respond. A polished interface can look more authoritative than the model behind it, so labs posted with SciViD items must meet the following requirements.

Status label: the first screen of the lab carries one of two labels: “Quantitative model: checked against [benchmark]” or “Illustrative model: not validated”. An Illustrative lab may support explanation but not claims about results.
Model statement: the governing equations, assumptions, and parameter values with SI units and their sources are given in the manuscript and can be reached from the lab.
Benchmark check: a Quantitative lab must reproduce at least one benchmark, such as a limiting case, an analytical solution or a published experimental result, and the manuscript must report the comparison.
Range of validity: parameter controls are limited to the range in which the model is expected to hold, or values outside that range are flagged on screen.
Authors’ understanding: the authors must be able to explain the model’s equations, assumptions and limitations in their own words, in the video abstract or when asked by the editors. Labs built with AI coding tools are acceptable when that use is disclosed in the AI Use Statement and the authors have checked the model themselves. A lab the authors cannot explain is posted only as an Illustrative model, or is declined.
Student authors: for Emerging Scholars submissions, the teacher or mentor confirms in the contribution statement that they have reviewed the benchmark check.
Versions: the code of each posted version is preserved unchanged. Errors are corrected in a new version, as set out in section 5 of the Integrity Guide.

Example titles

Illustrative only; not published papers.

“Self-Propelled Janus Micromotors Near Walls: A Brownian Dynamics Study”
“Droplet Breakup in a Microfluidic T-Junction: Volume-of-Fluid Simulations Across Capillary Numbers”
“Accretion Disk Instabilities Around a Black Hole: A Magnetohydrodynamic Simulation”
“Predicting the Mechanical Properties of Nanoporous Materials with Machine-Learned Interatomic Potentials”
“Molecular Dynamics of Lipid Bilayers: A Visual Review of Methods and Force Fields”

Video lets readers see how a model behaves, not only what it predicts. Combined with transparent methods and shared code, it can make computational work easier to evaluate and to reproduce.

Label	Meaning	Minimum evidence
Validated	The method reproduces an independent reference, and the result is converged	Benchmark comparison, convergence or repeatability test, documented source data
Simplified estimate	An order-of-magnitude or reduced-model result with stated assumptions	Assumptions listed, validity range stated, no claim beyond that range
Conceptual	An illustration of an idea, mechanism or scenario	Clearly marked as illustrative; not used as evidence

Mistake	How to detect it	How to handle it
Model used outside its regime (e.g., continuum flow where the gas is rarefied, inertia included at very low Reynolds number, Brownian motion neglected for sub-micrometer particles)	Compute the relevant dimensionless numbers (Reynolds, Knudsen, Péclet, Biot) for your parameters	State the regime explicitly; choose a model valid in it; cite the source that defines the validity range
Unit and dimension errors (mixing µm and m, °C and K, per-minute and per-second rates)	Check that every equation is dimensionally consistent; run one case by hand	Work in SI units internally; convert only for display; label every axis and value with units
Wrong or unstated boundary and initial conditions	Ask: what happens at each wall, inlet, outlet and at t = 0?	Write them out in the methods; justify each against the physical system
No comparison with a known solution	Ask: has this code ever reproduced a result I did not produce myself?	Reproduce an analytical limit or a published benchmark first; show the comparison
No convergence test	Results change when the mesh, time step or sample size is refined	Refine at least twice; report the change in the key quantity; use the converged setting
AI-generated code accepted without checking	The author cannot explain each line or each parameter	Read and comment the code; test it on a case with a known answer; record what the AI tool produced in the AI-use log
Parameter values without a source	A value appears in a table or narration with no reference	Give a reference, a measurement or an explicit assumption for every parameter
Too few samples for stochastic results	Brownian or Monte Carlo results vary noticeably between runs	Run multiple independent realizations; report the mean and spread

Mistake	Example	Correct handling
Overclaiming	“This device could revolutionize drug delivery” from a single simulation	State what was shown, under which assumptions, and what remains untested
Correlation presented as mechanism	Two quantities change together, so one is said to cause the other	Say “is associated with”; propose the mechanism as a hypothesis and state how to test it
Estimate presented as validated	An order-of-magnitude force estimate narrated as a measured result	Use the correct status label in the text, on screen and in the narration
Missing uncertainty	A single number with no error bar or range	Report the spread across runs, parameter sensitivity, or the range of validity
Selected parameters	Only the parameter set that “works” is shown	Show the parameter sweep, including cases where the effect disappears
Novelty not checked	“For the first time” without a literature search	Search the literature; cite prior work; describe precisely what is new
Extrapolation beyond the model	A result at the micrometer scale is narrated as applying to the human body	Restrict claims to the scale and conditions modeled

Mistake	Why it matters	Correct handling
Conceptual animation mistaken for evidence	A 3D render or generative-video shot looks like a result	Mark such shots on screen as “Conceptual illustration”; keep data-derived visuals visually distinct
Generative video showing physically impossible behavior	AI video tools do not obey physics; motion, scale and timing may be wrong	Use generative footage only for non-evidentiary context; never to show a result
Plots without units, scale bars or error bars	The viewer cannot judge magnitude or reliability	Label axes with quantity and SI unit; add scale bars to images; show uncertainty
Misleading color maps	Rainbow-type maps create false boundaries and are hard to read for colorblind viewers	Use perceptually uniform maps (e.g., viridis, cividis); show the color bar with units
Truncated or inconsistent axes	Small differences look large; panels cannot be compared	Start axes at zero where magnitudes are compared; use the same scale across panels
Narration diverging from the data	The voiceover states a value or trend the figure does not show	Script the narration from the results file; check every number spoken against the source
Time and length scales not shown	Slowed or accelerated motion, or magnified objects, mislead viewers	Display time stamps, playback speed and scale bars
Too much on screen at once	Viewers cannot follow equations, plots and narration simultaneously	One idea per scene; give equations time to be read

Topic	Resource	What it gives you
Reproducible computation	Sandve et al. (2013), Ten Simple Rules for Reproducible Computational Research, PLOS Comput. Biol. 9, e1003285	Practical rules: track how each result was produced, record versions and random seeds, store the raw data behind plots
Figures	Rougier, Droettboom and Bourne (2014), Ten Simple Rules for Better Figures, PLOS Comput. Biol. 10, e1003833	Designing figures for a given audience, message and medium
Color maps	Crameri, Shephard and Heron (2020), The misuse of colour in science communication, Nat. Commun. 11, 5444	Why rainbow and red–green maps distort data; how to choose accessible, perceptually uniform maps
Units	BIPM, The International System of Units (SI Brochure, 9th edition)	Authoritative definitions and rules for writing SI units and quantities
AI and authorship	COPE position statement: Authorship and AI tools	Why AI tools cannot be authors and what authors must disclose
Coding skills	Software Carpentry lessons	Free introductory lessons on the Unix shell, Git, and Python or R
Archiving data and code	Zenodo	A free general-purpose repository operated by CERN that assigns a DOI to each deposit

SciViD

Computational Models and Simulations

Common Mistakes in Video Preprints

1. Purpose and scope

Result status labels

2. Common mistakes in modeling and computation

3. Common mistakes in interpretation and claims

4. Common mistakes in visualization and video

5. Less obvious mistakes

6. Integrity, authorship and AI disclosure

7. Pre-submission checklist

Model and computation

Results and claims

Video

Integrity and reproducibility

8. Where to find help