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Subsequently, a two-step or nested proper orthogonal decomposition (POD) technique was used to generate the reduced basis (RB) functions and the corresponding projection coefficients within the RB space. The high-order dynamic mode decomposition (HODMD) method leveraged these corresponding coefficients to predict the projection coefficients at all training parameters over a time region beyond the training domain. Instead of direct regression and interpolating new parameters, the predicted projection coefficients were reorganized into a three-dimensional tensor, which was then decomposed into time- and parameter-dependent components through the canonical polyadic decomposition (CPD) method. Gaussian process regression (GPR) was then used to approximate the relationship between the time\/parameter values and the above components. Finally, the reduced-order solutions at new time\/parameter values were quickly obtained through a linear combination of the POD modes and the approximated projection coefficients. Numerical experiments were presented to evaluate the performance of the method in the case of plane wave scattering.&lt;\/p&gt;<\/jats:p>","DOI":"10.3934\/nhm.2024056","type":"journal-article","created":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T12:31:47Z","timestamp":1731501107000},"page":"1309-1335","source":"Crossref","is-referenced-by-count":2,"title":["A data-driven reduced-order modeling approach for parameterized time-domain Maxwell's equations"],"prefix":"10.3934","volume":"19","author":[{"given":"Mengjun","family":"Yu","sequence":"first","affiliation":[]},{"given":"Kun","family":"Li","sequence":"additional","affiliation":[]}],"member":"2321","reference":[{"key":"key-10.3934\/nhm.2024056-1","doi-asserted-by":"crossref","unstructured":"W. F. 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