A probabilistic ROM framework calibrates correction factors for a generalized one-fiber model using Bayesian inference on full-order isogeometric cardiac data and uses Gaussian processes for online prediction with uncertainty quantification.
Nature Reviews Cardiology 16 (2): 100–111
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The paper reduces a broad set of prompt engineering techniques to six core approaches and applies them to life sciences use cases while addressing common LLM pitfalls.
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A probabilistic reduced-order modeling framework for patient-specific cardio-mechanical analysis
A probabilistic ROM framework calibrates correction factors for a generalized one-fiber model using Bayesian inference on full-order isogeometric cardiac data and uses Gaussian processes for online prediction with uncertainty quantification.
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The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences
The paper reduces a broad set of prompt engineering techniques to six core approaches and applies them to life sciences use cases while addressing common LLM pitfalls.