AI RESEARCH
Solving Inverse Parametrized Problems via Finite Elements and Extreme Learning Networks
arXiv CS.LG
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ArXi:2602.14757v2 Announce Type: replace-cross We develop an interpolation-based modeling framework for parameter-dependent partial differential equations arising in control, inverse problems, and uncertainty quantification. The solution is discretized in the physical domain using finite element methods, while the dependence on a finite-dimensional parameter is approximated separately.