Kinetic theory and mathematical modeling have emerged as indispensable tools for elucidating the collective behaviour of complex systems. This discipline applies statistical mechanics to a diverse ...

Topic: Uncertainty Quantification for Scientific Machine Learning Modeling and Simulations Uncertainty quantification (UQ) in scientific computing research is increasingly important for keeping pace ...

In fields ranging from immunology and ecology to economics and thermodynamics, multi-scale complex systems are ubiquitous. They are also notoriously difficult to model. Conventional approaches ...

Stochastic modeling can help us to predict the future behavior of complex processes, which are non-Markovian. In order to simulate a non-Markovian process, a memory is of necessity to store a ...

When most people think of computer simulations, they imagine sleek graphics or Hollywood-style animations. But for Tayfun ...

MIT researchers use large language models to flag problems in complex systems The approach can detect anomalies in data recorded over time, without the need for any training. Date: August 13, 2024 ...

Data-driven complex systems modeling approaches could overcome the drawbacks of static measures and allow us to quantitatively model the dynamic recovery trajectories and intrinsic resilience ...

Empowering the design, modeling, and verification of complex 6G systems.