We present HINTS, a Hybrid, Iterative, Numerical, and Transferable Solver that combines Deep Operator Networks (DeepONet) with classical numerical methods to efficiently solve partial differential equations (PDEs). By leveraging the complementary strengths of DeepONet’s spectral bias for representing low-frequency components and relaxation or Krylov methods’ efficiency at resolving high-frequency modes, HINTS balances convergence rates across eigenmodes. The HINTS is highly flexible, supporting large-scale, multidimensional systems with arbitrary discretizations, computational domains, and boundary conditions, and can also serve as a preconditioner for Krylov methods. To demonstrate the effectiveness of HINTS, we present numerical experiments on parametric PDEs in both two and three dimensions.

Dimensionality reduction represents the process of generating a low dimensional representation of high dimensional data. In this talk, I explain what dimensionality reduction is and shortly mention formation control. After that, I will introduce a nonlinear dynamical system designed for dimensionality reduction. I briefly discuss mathematical properties of the model and demonstrate numerical experiments on both synthetic and real datasets.

Virtual element method (VEM) is a generalization of the finite element method to general polygonal (or polyhedral) meshes. The term ‘virtual’ reflects that no explicit form of the shape function is required. The discrete space on each element is implicitly defined by the solution of certain boundary value problem. As a result, the basis functions include non-polynomials whose explicit evaluations are not available. In implementation, these basis functions are projected to polynomial spaces. In this talk, we briefly introduce the basic concepts of VEM. Next, we introduce mixed virtual volume methods (MVVM) for elliptic problems. MVVM is formulated by multiplying judiciously chosen test functions to mixed form of elliptic equations. We show that MVVM can be converted to SPD system for the pressure variable. Once the primary variable is obtained, the Darcy velocity can be computed locally on each element.