Artificial intelligence/machine learning-driven modeling reduces time to market for faster design technology co-optimization development..
New research from Sandia National Laboratories suggests that brain-inspired neuromorphic computers are just as adept at ...
This project builds a machine-learning surrogate model using data generated from a quasi-6-DoF physics simulator replacing numerical integration with a neural network that can predict full 3-D ...
Department of Computing, Imperial College London, London SW7 2AZ, U.K. Sargent Centre for Process Systems Engineering, Imperial College London, London SW7 2AZ, U.K. Department of Computing, Imperial ...
Abstract: Interturn short-circuit (ITSC) faults in transformers can result in catastrophic failures if they are not identified and isolated in time, thus, relay protection devices with properly ...
Introduction: Digital twins of the respiratory system have shown promise in predicting the patient-specific response of lungs connected to mechanical ventilation. However, modeling the spatiotemporal ...
Abstract: The machine learning method for surrogate modeling is a keystone in surrogate model-assisted evolutionary algorithms (SAEAs). The current arguably most widely used surrogate modeling methods ...
Increasingly, AI models are able make short-term weather forecasts with surprising accuracy. But neural networks only predict based on patterns from the past—what happens when the weather does ...
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