Gauss-Newton optimization 1inverse problems 1parameter estimation 1reduced-order modeling 1tensor-train decomposition 1
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math.NA2026
Tensor-Based Reduced-Order Modeling for Optimization-Based Inverse Problems
Sahidul Islam, Andreas Mang, Maxim Olshanskii
The paper introduces a tensor-train based reduced-order modeling framework that directly approximates the parameter-to-observation map for optimization-based inverse problems, enab…
math.NA2026
Tensorial Reduced-Order Models for Parametric Coupled Reaction-Diffusion Systems: Application to Brain Tumor Growth Modeling
Asikul Islam, Md Rezwan Bin Mizan, Maxim Olshanskii +1
We construct efficient surrogate models for parametric forward operators arising in brain tumor growth simulations, governed by coupled semilinear parabolic reaction-diffusion syst…