Reconstructing high-dimensional Hilbert-valued functions via compressed sensing
For researchers solving parameterized PDEs with high-dimensional inputs, this work provides a theoretically grounded method that reduces sample complexity while maintaining accuracy.
This paper develops a sparse polynomial technique for approximating high-dimensional Hilbert-valued functions, with application to parameterized PDEs. The method achieves joint sparse recovery with minimal sample complexity, outperforming existing approaches in numerical experiments on high-dimensional elliptic PDEs.
We present and analyze a novel sparse polynomial technique for approximating high-dimensional Hilbert-valued functions, with application to parameterized partial differential equations (PDEs) with deterministic and stochastic inputs. Our theoretical framework treats the function approximation problem as a joint sparse recovery problem, where the set of jointly sparse vectors is possibly infinite. To achieve the simultaneous reconstruction of Hilbert-valued functions in both parametric domain and Hilbert space, we propose a novel mixed-norm based $\ell_1$ regularization method that exploits both energy and sparsity. Our approach requires extensions of concepts such as the restricted isometry and null space properties, allowing us to prove recovery guarantees for sparse Hilbert-valued function reconstruction. We complement the enclosed theory with an algorithm for Hilbert-valued recovery, based on standard forward-backward algorithm, meanwhile establishing its strong convergence in the considered infinite-dimensional setting. Finally, we demonstrate the minimal sample complexity requirements of our approach, relative to other popular methods, with numerical experiments approximating the solutions of high-dimensional parameterized elliptic PDEs.