Michigan State University
Mahdi Masmoudi
Ph.D. researcher working on scientific machine learning, inverse problems, and monitoring.
Latest paper
Mechanics-Informed Autoencoder Enables Automated Detection and Localization of Unforeseen Structural Damage
Nature Communications, 2024. A mechanics-informed autoencoder detected damage and localized early cracks more accurately than baseline autoencoder models.
About
My research focuses on scientific machine learning and inverse problems. I am interested in parameter estimation, anomaly detection, and learning from limited or irregular observations.
Much of the work uses physical structure and governing equations to make models more reliable and easier to interpret in monitoring settings.
Research
Full HAL pageSelected work on anomaly detection, parameter-field inference, and learning with sparse observations.
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Nature Communications · 2024
01
Mechanics-Informed Autoencoder Enables Automated Detection and Localization of Unforeseen Structural Damage
Nature Communications, 2024. A mechanics-informed autoencoder detected damage and localized early cracks more accurately than baseline autoencoder models.
View paper -
NeurIPS · 2025
02
Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements
A framework for recovering parameter fields in coupled PDE systems from scarce measurements while preserving the structure of the governing physics.
View paper -
NeurIPS · 2024
03
ParaFIND: Parameter Field Inference on Non-Uniform Domains using Neural Network
A neural approach for inferring parameter fields on non-uniform domains from sparse and irregular observations.
View paper -
ICLR · 2024
04
Estimating Field Parameters in Multiphysics Governing Equations from Scarce Observations
NeuralFD estimates field parameters in multiphysics governing equations from limited observations and incomplete spatial coverage.
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