The phrase "across multiple models" is correct and usable in written English.
It can be used when discussing comparisons, analyses, or applications that involve more than one model, such as in research, data analysis, or machine learning contexts.
Example: "The results were consistent across multiple models, indicating a robust finding."
Alternatives: "through various models" or "among different models".
Ferguson, A.R. et al. Derivation of multivariate syndromic outcome metrics for consistent testing across multiple models of cervical spinal cord injury in rats.
In particular, we discuss model transformation, model ontologies, and multimodeling, with applications to construction of large models (e.g. MapReduce), model optimization, and consistency management across multiple models.
Their purpose is to build theories that help our students think through seemingly incommensurable models and instead of narrowing their world and adopting one model, to embrace the breadth and integrate across multiple models.
As an instrument designed to be applicable across multiple models of anticoagulation management, the DASS could be crucial in the scientific comparison between those models of care.
Ensembles means averaging across multiple models that rely on different data or reweighted data and/or employ different models or methods.
But recalls still happen, and can involve huge numbers of vehicles because of the industry's tendency to use the same components across multiple models.
The ISHC demonstrates the feasibility of PMMs and their usefulness across multiple model use cases.
This report demonstrates the feasibility of PMMs and their usefulness across multiple model use cases.
We were able to quantify local source contributions relative to background concentrations across multiple model configurations.
We present evidence that NLK influences the aggregation and toxicity of polyQ-expanded AR across multiple model systems, using cell culture, Drosophila, and mouse.
We achieved this by comparing the magnitudes of evolutionary changes in unharvested populations with those in harvested populations across multiple model runs, so as to estimate the probabilities, separately for each evolving trait, that the former exceed the latter.
