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math.NA2026

A review of shape-morphing solutions and evolutional neural networks for spatiotemporal dynamics

Mohammad Farazmand

Shape-morphing solutions (SMS) refer to a class of approximate solutions of partial differential equations (PDEs) with the distinguishing feature that they depend nonlinearly on a…

math.NA2025

Discrete Empirical Interpolation Method with Upper and Lower Bound Constraints

Louisa B. Ebby, Mohammad Farazmand

Discrete Empirical Interpolation Method (DEIM) is a simple and effective method for reconstructing a function from its incomplete pointwise observations. However, applying DEIM to…

math.NA2025

Bridging the Gap Between Deterministic and Probabilistic Approaches to State Estimation

Lev Kakasenko, Alen Alexanderian, Mohammad Farazmand +1

We consider the problem of state estimation from limited discrete and noisy measurements. In particular, we focus on modal state estimation, which approximates the unknown state of…

math.NA2025

Sequential data assimilation for PDEs using shape-morphing solutions

Zachary T. Hilliard, Mohammad Farazmand

Shape-morphing solutions (also known as evolutional deep neural networks, reduced-order nonlinear solutions, and neural Galerkin schemes) are a new class of methods for approximati…

math.NA2024

Sparse Discrete Empirical Interpolation Method: State Estimation from Few Sensors

Mohammad Farazmand

Discrete empirical interpolation method (DEIM) estimates a function from its incomplete pointwise measurements. Unfortunately, DEIM suffers large interpolation errors when few meas…