8 papers
Supervised Deep Multimodal Matrix Factorization for Interpretable Brain Network Analysis
Amjad Seyedi, Lifang He, Songlin Zhao +2
We present Supervised Deep Multimodal Matrix Factorization (SD3MF), an interpretable framework for integrative brain network analysis that generalizes Symmetric Nonnegative Matrix…
Stochastic Galerkin Method and Hierarchical Preconditioning for PDE-constrained Optimization
Zhendong Li, Akwum Onwunta, BedÅich SousedÃk
We develop efficient hierarchical preconditioners for optimal control problems governed by partial differential equations with uncertain coefficients. Adopting a discretize-then-op…
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…
Fast-forwarding quantum algorithms for linear dissipative differential equations
Dong An, Akwum Onwunta, Gengzhi Yang
We establish improved complexity estimates of quantum algorithms for linear dissipative ordinary differential equations (ODEs) and show that the time dependence can be fast-forward…
Tensor train solution to uncertain optimization problems with shared sparsity penalty
Harbir Antil, Sergey Dolgov, Akwum Onwunta
We develop both first and second order numerical optimization methods to solve non-smooth optimization problems featuring a shared sparsity penalty, constrained by differential equ…
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…