We introduce generative models for accelerating simulations of high-dimensional systems
through learning and evolving their effective dynamics. In the proposed Generative Learning …

Turbulent flows, characterized by their chaotic and stochastic nature, have historically
presented formidable challenges to predictive computational modeling. Traditional eddy …

Eddy-resolving turbulence simulations are essential for understanding and controlling
complex unsteady fluid dynamics, with significant implications for engineering and scientific …

Predicting high-fidelity ground motions for future earthquakes is crucial for seismic hazard
assessment and infrastructure resilience. Conventional empirical simulations suffer from …

We introduce generative models for accelerating simulations of complex systems through
learning and evolving their effective dynamics. In the proposed Generative Learning of …

Eddy-resolving turbulence simulations require stochastic inflow conditions that accurately
replicate the complex, multi-scale structures of turbulence. Traditional recycling-based …

High-fidelity engineering simulations can impose an enormous computational burden,
hindering their application in design processes or other scenarios where time or …

This study introduces the Conditional Neural Field Latent Diffusion (CoNFiLD) model, a
novel generative learning framework designed for rapid simulation of intricate …

Current approaches using sequential networks have shown promise in estimating field
variables for dynamical systems, but they are often limited by high rollout errors. The …

This paper studies the problem of modeling multi-agent dynamical systems, where agents
could interact mutually to influence their behaviors. Recent research predominantly uses …