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the integration of AI components transforms the nature of software systems (SE4AI). From an architectural perspective, the research investigates how the inclusion of AI elements—such as retrainable ML models, LLMs
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on developing methods for the verification and validation of systems that embed machine learning or generative models, addressing challenges such as non-determinism, data drift, and explainability. The project
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learning models—alters the way software systems evolve (SE4AI). A strong focus will be on the post-deployment lifecycle of ML components, including drift detection, model decay, retraining triggers, and
Searches related to molecular modeling
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