22 citations · 33 across the 7 of their papers we have counts for
3 papers · 1 filter
Learning Physics from an Imperfect Ancestor
S. Mohammad Mousavi, Teeratorn Kadeethum, Nikolaos Bouklas +1
Neural operators evaluate parametric partial differential equations cheaply but degrade sharply outside their training distribution. Physics-informed neural networks avoid dependen…
Partial Differential Equations in the Age of Machine Learning: A Critical Synthesis of Classical, Machine Learning, and Hybrid Methods
Mohammad Nooraiepour, Jakub Wiktor Both, Teeratorn Kadeethum +1
Partial differential equations (PDEs) govern physical phenomena across the full range of scientific scales, yet their computational solution remains one of the defining challenges…
Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer
Teeratorn Kadeethum, Daniel O'Malley, Youngsoo Choi +2
Data-driven modeling can suffer from a constant demand for data, leading to reduced accuracy and impractical for engineering applications due to the high cost and scarcity of infor…