Science

A new framework leverages the power of physics-informed neural networks to accurately simulate and invert viscoacoustic wave propagation, even with limited data.

New research explores whether tracking macroeconomic indicators can offer valuable early warnings for public health challenges, particularly related to system capacity and workforce strain.

A new approach uses neural networks to refine covariance matrix estimation, leading to demonstrably lower portfolio risk compared to traditional methods.

A new multi-agent system leverages the power of large language models and traditional machine learning to dramatically improve the accuracy and interpretability of traffic accident severity predictions.

A new wave of research is transforming uncertainty quantification from a diagnostic tool into a powerful control signal for large language models, improving reasoning and enabling more robust AI agents.

New research demonstrates how explainable AI techniques can improve the performance and trustworthiness of machine learning models used in critical industrial applications.

A large-scale international evaluation reveals significant vulnerabilities in AI agents tasked with complex actions, highlighting critical gaps in safety methodologies.

New research reveals that commonly used financial models underestimate cryptocurrency risk, potentially leaving investors unprepared for significant losses.

A new approach uses artificial intelligence to automatically identify and categorize potential risks disclosed in company filings.

As cloud infrastructure grows, so does the deluge of alerts, demanding intelligent systems to prioritize critical issues and reduce operator burnout.