16 citations · 41 across the 17 of their papers we have counts for
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Deep learning methods for inverse problems using connections between proximal operators and Hamilton-Jacobi equations
Oluwatosin Akande, Gabriel P. Langlois, Akwum Onwunta
Inverse problems are important mathematical problems that seek to recover model parameters from noisy data. Since inverse problems are often ill-posed, they require regularization…
Stochastic Galerkin Method and Hierarchical Preconditioning for PDE-constrained Optimization
Zhendong Li, Akwum Onwunta, Bedřich Sousedík
We develop efficient hierarchical preconditioners for optimal control problems governed by partial differential equations with uncertain coefficients. Adopting a discretize-then-op…
Quantum Differential Equation Solvers with Low State Preparation Cost: Eliminating the Time Dependence in Dissipative Equations
Gengzhi Yang, Akwum Onwunta, Dong An
Linear dissipative differential equation is a fundamental model for a large number of physical systems, such as quantum dynamics with non-Hermitian Hamiltonian, open quantum system…
Convex-concave splitting for the Allen-Cahn equation leads to -slow movement of interfaces
Patrick Dondl, Akwum Onwunta, Ludwig Striet +1
The convex-concave splitting discretization of the Allen-Cahn is easy to implement and guaranteed to be energy decreasing even for large time-steps. We analyze the time-stepping sc…
Momentum-based minimization of the Ginzburg-Landau functional on Euclidean spaces and graphs
Oluwatosin Akande, Patrick Dondl, Kanan Gupta +2
We study the momentum-based minimization of a diffuse perimeter functional on Euclidean spaces and on graphs with applications to semi-supervised classification tasks in machine le…