2 papers
cs.LG2025
Deep Belief Markov Models for POMDP Inference
Giacomo Arcieri, Konstantinos G. Papakonstantinou, Daniel Straub +1
This work introduces a novel deep learning-based architecture, termed the Deep Belief Markov Model (DBMM), which provides efficient, model-formulation agnostic inference in Partial…
physics.comp-ph2024
Response Estimation and System Identification of Dynamical Systems via Physics-Informed Neural Networks
Marcus Haywood-Alexander, Giacomo Arcieri, Antonios Kamariotis +1
The accurate modelling of structural dynamics is crucial across numerous engineering applications, such as Structural Health Monitoring (SHM), seismic analysis, and vibration contr…