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Trustworthy AI for Science

Methods for robust, interpretable and well-calibrated machine learning in scientific research workflows.

Open for submissionsSubmission deadline: 31 December 2026Hosted by Applied Artificial Intelligence Letters
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Articles in this collection

Methods

Calibrated uncertainty estimates for machine-learning surrogates in scientific simulation

Lucas Ferreira

Machine-learning surrogates are increasingly used to accelerate expensive physical simulations, but their predictive uncertainty is frequently miscalibrated. We introduce a conformal ensemble procedure that provides finite-sample coverage guarantees without retraining the base model. Across five benchmark simulation tasks, the method achieves nominal coverage with intervals up to 35% narrower than existing baselines.

Original Research

Energy-efficient event-based vision with neuromorphic recurrent architectures

Daniel Osei, Lucas Ferreira

Event cameras generate sparse, asynchronous data streams well suited to neuromorphic processing. We propose a recurrent spiking architecture that achieves competitive accuracy on gesture and optical-flow benchmarks while consuming an order of magnitude less energy than conventional convolutional baselines on equivalent hardware.