The large and catastrophic wildfires have been increasing across the globe in recent decade, highlighting importance of simulating forecasting fire dynamics near real-time. This is extremely challenging due to complexities physical models geographical features. Running physics-based simulations for wildfire events real-time are computationally expensive, if not infeasible. In this work, we deve...

Abstract We present a fast method for nonlinear data-driven model reduction of dynamical systems onto their slowest nonresonant spectral submanifolds (SSMs). While the recently proposed reduced-order modeling SSMLearn uses implicit optimization to fit submanifold data and reduce dynamics normal form, here, we reformulate these tasks as explicit problems under certain simplifying assumptions. In...

Abstract We develop a data‐driven clustering method to evaluate velocity model using surface wave dispersion. This is done by first computing theoretical dispersion curves for 1‐D profiles of all the grid locations and then splitting resulting into certain number groups via K‐means clustering. The observed are also clustered following same procedure assessed comparing spatial patterns obtained ...