Combining Stochastic Parameterized Reduced-Order Models with Machine Learning for Data Assimilation and Uncertainty Quantification with Partial Observations

Speaker: Changhong Mou (UWM) Dates: 2023/09/29 Time: 4:10-5:10 (EST) Location: Zoom, link Abstract: A hybrid data assimilation algorithm is developed for complex dynamical systems with partial observations. The method starts with applying a spectral decomposition to the entire spatiotemporal fields,…

The semi-implicit DLN algorithm for the Navier-Stokes equations

Speaker: Wenlong Pei (OSU) Dates: 2023/09/21 Location: MW105 Abstract:Dahlquist, Liniger, and Nevanlinna design a family of one-leg, two-step methods (the DLN method) that is second order,A−andG−stable for arbitrary, non-uniform time steps. Recently, the implementation of the DLN method can be simplified…

Efficient ensemble methods for simulating groundwater-surface flows

Speaker: Ying Li (OSU) Dates: 2023/09/15 Location: MW154 Abstract: We propose and analyze a series of efficient, unconditionally stable, ensemble methods for stimulating groundwater-surface flows governed by the Stokes-Darcy model. For systems like the surface-groundwater flows, predictive simulations must account…

Deep learning for parameter estimation

Speaker:  Caroline Tatsuoka (OSU) Dates: 2023/10/05 Location: MA105 Abstract: We present methods to obtain estimates to unknown parameters of dynamical systems via data driven methods using deep neural networks (DNNs). An inverse map from the solution space to the parameter…