We attempt to use algorithmically drawn boxes on collisions in the shallow water equations as training data for identification of an ODE system governing the dynamics of colliding currents.
Supervised by François Blanchette and Nathan Willis.
We proposed a model quantifying inter–district inequalities, minimized using constrained multi-objective optimization. Application to Alameda County reduced the mean-square deviation from average racial distributions by 83.1%.
Supervised by Kyle Wright and Cory Mccullough.
We created software to model the spread of turbidity currents generated by deep-sea mining. The model uses the Box Model derived by height– and width–averaging the Navier-Stokes equations.
Supervised by François Blanchette and Nathan Willis.
Augmented Box Model for Colliding Turbidity Currents
Reducing Inter-District Inequality as a Constrained Multi-Objective Optimization Problem
Box Model Simulations of Turbidity Currents
2023 Central Valley Regional SIAM Student Chapter Conference
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