Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

Spiking Neural Networks (SNNs) process information through discrete, time-dependent spikes, closely mimicking the dynamics of biological neurons. This temporal coding enables SNNs to capture rich ...

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This group project explores the use of neural networks to model avalanche hazard forecasts using a 15-year dataset from the Scottish Avalanche Information Service (SAIS). Our group has been assigned ...

Abstract: Recent advances in hardware and software technology have made it possible to implement more resourcedemanding deep learning algorithms in constrained hardware environments. This creates ...

Abstract: The emergence of Deep Learning compilers provides automated optimization and compilation across Deep Learning frameworks and hardware platforms, which enhances the performance of AI service ...

The scary thing about Dwight "D.J." Mims when it comes to coaching football - he has only scratched the surface. But now, he's found one of those rare jobs he can really establish his name by leading ...

The integration of neural networks into steelmaking has revolutionised process control, quality assurance and energy efficiency across primary steelmaking routes. These data-driven models are deployed ...

Got it now: “Graph Neural Networks (GNN) are a general class of networks that work over graphs. By representing a problem as a graph — encoding the information of individual elements as nodes and ...