Scientific machine learning for closure models in multiscale problems: A review
Closure problems are omnipresent when simulating multiscale systems, where some
quantities and processes cannot be fully prescribed despite their effects on the simulation's …
quantities and processes cannot be fully prescribed despite their effects on the simulation's …
A causality-based learning approach for discovering the underlying dynamics of complex systems from partial observations with stochastic parameterization
Discovering the underlying dynamics of complex systems from data is an important practical
topic. Constrained optimization algorithms are widely utilized and lead to many successes …
topic. Constrained optimization algorithms are widely utilized and lead to many successes …
Data-driven stochastic closure modeling via conditional diffusion model and neural operator
Closure models are widely used in simulating complex multiscale dynamical systems such
as turbulence and the earth system, for which direct numerical simulation that resolves all …
as turbulence and the earth system, for which direct numerical simulation that resolves all …
A physics-informed data-driven algorithm for ensemble forecast of complex turbulent systems
A new ensemble forecast algorithm, named the physics-informed data-driven algorithm with
conditional Gaussian statistics (PIDD-CG), is developed to predict the probability density …
conditional Gaussian statistics (PIDD-CG), is developed to predict the probability density …
A Stochastic Precipitating Quasi-Geostrophic Model
Efficient and effective modeling of complex systems, incorporating cloud physics and
precipitation, is essential for accurate climate modeling and forecasting. However …
precipitation, is essential for accurate climate modeling and forecasting. However …
Developing an advanced neural network and physics solver coupled framework for accelerating flow field simulations
Computational fluid dynamics simulation accounts for a large number of workloads in the
numerical design optimization of aerodynamics problems. In this paper, we develop AFFNet …
numerical design optimization of aerodynamics problems. In this paper, we develop AFFNet …