PINNs are applied in a proof-of-concept to mitigate attacks on liquid pump controllers in water distribution networks by incorporating physical flow laws into network training.
Secure state estimation for cyber-physical systems under sensor attacks: A satisfiability modulo theory approach
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CR 1years
2020 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Physics-Informed Neural Networks for Securing Water Distribution Systems
PINNs are applied in a proof-of-concept to mitigate attacks on liquid pump controllers in water distribution networks by incorporating physical flow laws into network training.