1 citations · 1 across the 6 of their papers we have counts for
6 papers
Non-Asymptotic Variational Learning for Monotone Nonlinear Multiscale Elliptic Equations: Scale-Robust Primal-Dual Bounds and Strong-Form Statistical Ill-Conditioning
Ronald Katende
We develop a non-asymptotic approximation, sampling, and finite-iteration optimization theory for variational physics-informed approximation of uniformly monotone nonlinear multisc…
No-Harm Physics-Informed Inverse Learning with Residual-Calibrated Uncertainty
Ronald Katende
Physics-informed learning is increasingly used for partial differential equation (PDE)-governed inverse problems, but its reliability remains difficult to certify. This paper devel…
A Frobenius-Optimal Projection for Enforcing Linear Conservation in Learned Dynamical Models
John M. Mango, Ronald Katende
We consider the problem of restoring linear conservation laws in data-driven linear dynamical models. Given a learned operator and a full-rank constraint matrix e…
Preserving Extreme Singular Values with One Oblivious Sketch
John M. Mango, Ronald Katende
We study when a single linear sketch can control the largest and smallest nonzero singular values of every rank- matrix. Classical oblivious embeddings require $s=Θ(r/\varepsilo…
Adaptive Error-Bounded Hierarchical Matrices for Efficient Neural Network Compression
John Mango, Ronald Katende
This paper introduces a dynamic, error-bounded hierarchical matrix (H-matrix) compression method tailored for Physics-Informed Neural Networks (PINNs). The proposed approach reduce…
Curvature-Adaptive Perturbation and Subspace Descent for Robust Saddle Point Escape in High-Dimensional Optimization
Ronald Katende, Henry Kasumba
High-dimensional non-convex optimization problems in engineering design, control, and learning are often hindered by saddle points, flat plateaus, and strongly anisotropic curvatur…