[Pagsusuri] Data-Driven Science and Engineering (Steven L. Brunton) Nabuod.

Data-Driven Science and Engineering (Steven L. Brunton)

- Amazon USA Store: https://www.amazon.com/dp/1009098489?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Data-Driven-Science-and-Engineering-Steven-L-Brunton.html

- Apple Books: https://books.apple.com/us/audiobook/data-driven-science-and-engineering-mastering-the/id1762587111?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree

- eBay: https://www.ebay.com/sch/i.html?_nkw=Data+Driven+Science+and+Engineering+Steven+L+Brunton+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1

- Shopee: https://asia.buys.trade/shopee_Data-Driven-Science-and-Engineering-Steven-L-Brunton.html
- Lazada: https://asia.buys.trade/lazada_Data-Driven-Science-and-Engineering-Steven-L-Brunton.html
- Tiktok: https://asia.buys.trade/tiktok_Data-Driven-Science-and-Engineering-Steven-L-Brunton.html

- Magbasa pa: https://tagalog.9natree.com/read/1009098489/

#SparseIdentificationofNonlinearDynamicsoSINDy #DynamicModeDecompositionoDMD #Reducedordermodeling #Reinforcementlearningparasacontrol #Physicsinformedmachinelearning #DataDrivenScienceandEngineering

Ang Data-Driven Science and Engineering ni Steven L. Brunton at J. Nathan Kutz ay isang teknikal na aklat-aralin tungkol sa paggamit ng datos upang umunawa, bumuo ng modelo, manghula, at kumontrol ng mga kumplikadong sistemang nagbabago sa oras. Ikalawang edisyon ito, na inilathala noong 2022, at pinagdudugtong nito ang machine learning, inilapat na matematika, dynamical systems, optimization, at control theory. Hindi ito simpleng gabay sa paggamit ng mga handang software package. Layunin nitong ipaliwanag kung bakit may saysay ang isang modelo, paano ito sinusuri, at kailan ito maaaring gamitin sa aktuwal na sistemang pisikal o inhinyeriya. Mula sa mga pamamaraan tulad ng dimensionality reduction at Dynamic Mode Decomposition, umaabot ang aklat sa Sparse Identification of Nonlinear Dynamics, reinforcement learning, at physics-informed machine learning. Ang diin ay nasa mga modelong sapat ang pagiging tumpak ngunit malinaw ding maipapaliwanag. May kasamang mga pagsasanay at online na code, datos, at lektura, kaya idinisenyo ito para sa pag-aaral na may kasabay na komputasyonal na pagsubok.