Science

A new framework combines physics-informed machine learning with anomaly detection to create cyber-resilient digital twins for safeguarding industrial control systems.

A new network-based approach reveals how events cluster together in irregular time series, offering insights into complex systems from heartbeats to turbulent flows.

New research reveals that the structure of AI governance, rather than the intelligence of the AI itself, is the key to preventing corruption in multi-agent systems.

Researchers have developed a novel artificial intelligence framework that accurately forecasts stress within hyperelastic materials, paving the way for more efficient simulations and designs.

A comprehensive review clarifies the landscape of deep learning methods for identifying unusual patterns in complex, evolving data streams.

Researchers have developed a technique to reliably elicit harmful responses from AI models, paving the way for more effective safety alignment and robust countermeasures.

A new framework actively audits federated learning networks to detect and neutralize adaptive backdoor attacks before they compromise the system.

A new error detection scheme bolsters the resilience of deep neural networks against hardware-based fault injection attacks, particularly in edge computing devices.

As large language models become increasingly integrated into scientific workflows, ensuring their reliability, safety, and security is paramount.

A new deep learning architecture efficiently tackles the complex problem of predicting multi-channel time series data.