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X-ORIGINAL-URL:https://academics.dii.unipd.it/phd
X-WR-CALDESC:Events for PHD in Industrial Engineering
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DTSTART:20250101T000000
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DTSTART;TZID=UTC:20260519T090000
DTEND;TZID=UTC:20260519T173000
DTSTAMP:20260525T045714
CREATED:20260413T133919Z
LAST-MODIFIED:20260421T151833Z
UID:3242-1779181200-1779211800@academics.dii.unipd.it
SUMMARY:Introduction to Model Order Reduction
DESCRIPTION:Course contentsModel Order Reduction (MOR) is a key technique for decreasing the computational complexity of mathematical models in numerical simulations. This course explores the relationship between MOR and metamodeling\, highlighting its versatile applications across all areas of mathematical modeling and engineering where high-fidelity simulations are required. \nThe curriculum introduces the main numerical approaches used to perform Model Order Reduction\, with a detailed focus on the Proper Orthogonal Decomposition (POD). Students will examine the theoretical foundations of the POD algorithm and its practical implementation. The course features a hands-on component where the POD algorithm is applied to accelerate time-domain simulations of thermal problems using MATLAB\, demonstrating the real-world impact of MOR on large-scale numerical simulations. \nLearning goalsUpon completion of the course\, participants will acquire a comprehensive understanding of the primary numerical techniques for Model Order Reduction. Specifically\, PhD students will develop the ability to apply these methods to dynamic models\, enhancing computational efficiency while maintaining high accuracy in simulation results. \nSuggested readings\nBenner P.\, Grivet-Talocia S.\, Quarteroni A.\, Rozza G.\, Schilders W.\, Magdeburg L. M. S. Model Order Reduction. Three volumes. Doi: 10.1515/9783110499001\nBenner\, P.\, Feng\, L. (2014). A Robust Algorithm for Parametric Model Order Reduction Based on Im- plicit Moment Matching. In: Quarteroni\, A.\, Rozza\, G. (eds)\nReduced Order Methods for  Modeling  and Computational Reduction. MS&A – Modeling\, Simulation and Applications\, vol 9. Springer\, Cham. https://doi.org/10.1007/978-3-319-02090-7_6\nFeng\, L.\, Yue\, Y.\, Banagaaya\, N. et al. Parametric modeling and model order reduction for (electro-)thermal analysis of nanoelectronic structures. J.Math.Industry 6\, 10 (2016).\nY. Liang\, H. Lee\, S. Lim\, W. Lin\, K. Lee\, and C. Wu. Proper orthogonal decomposition and its applica- tions—part i: Theory. Journal of Sound and Vibration\, vol. 252\, no. 3\, pp. 527–544\, 2002\nS. Brunton\, J. Nathan Kutz\, Data-Driven Science and Engineering. Doi: https://doi.org/10.1017/9781108380690
URL:https://academics.dii.unipd.it/phd/event/introduction-to-model-order-reduction/
LOCATION:Sede-V\, via Venezia 1\, Padova\, Padova\, 35131\, Italy
CATEGORIES:Event
ATTACH;FMTTYPE=image/jpeg:https://academics.dii.unipd.it/phd/wp-content/uploads/sites/58/2026/04/CDII_model_1-scaled.jpeg
ORGANIZER;CN="PhD Course in Industrial Engineering":MAILTO:dottorato.dii@unipd.it
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