ArticleMicromachines2026
Uncertainty-Aware Machine Learning for Compact-Model-Based Design and Identifiability Analysis of WSe
Article in Micromachines, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine-learning surrogates can accelerate transistor design, but optimized predictions require physical consistency and explicit treatment of surrogate uncertainty. Here, we develop an uncertainty-aware machine-learning framework for compact-model-based design of WSe
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.