Descrizione del progetto
Migliorare il monitoraggio e la risposta alle epidemie con reti neurali fisicamente informate
Le reti neurali fisicamente informate (PINN) integrano perfettamente dati e modelli fisici matematici, consentendo di prevedere una soluzione lontana dai dati sperimentali anche in scenari complessi, incerti e parzialmente compresi. Ciò li rende particolarmente adatti a prevedere l’evoluzione di un’epidemia su varie scale temporali e spaziali, facilitando la definizione di politiche efficaci. Con il sostegno del programma di azioni Marie Skłodowska-Curie, il progetto REACT sfrutterà le PINN per integrare i dati epidemiologici passati con il modello fisico di diffusione delle epidemie. In combinazione con strumenti teorici di sistema, l’approccio consentirà un monitoraggio epidemico ad anello chiuso in grado di affrontare efficacemente le incertezze, convalidando l’algoritmo di stima in tempo reale attraverso la previsione dei dati futuri e regolando di conseguenza il modello epidemico.
Obiettivo
The current COVID-19 crisis has highlighted the failure of existing epidemic monitoring techniques in timely predicting the epidemic situation and facilitating efficient policy recommendations. Because of being open-loop or linearization-based, these techniques cannot handle model and data uncertainties effectively. Designing a feedback mechanism to enable reliable, closed-loop epidemic monitoring is crucial but challenging because of the nonlinearity and heterogeneities of the epidemic spread process. The control mechanisms for epidemic mitigation are well-known, such as testing, lockdown, social distancing, etc. However, when, where, and to what extent should the health authority implement these policies depends on the accurate estimation and forecasting of the epidemic situation, which is very difficult with the classic observer design techniques. To alleviate the difficulties posed by these observers, an interdisciplinary approach of physics-informed neural network (PINN) in combination with system-theoretic tools is proposed in this project for closed-loop epidemic monitoring that can effectively cope with uncertainties. The task of PINN is to estimate the unknown nonlinearity (i.e. disease transmission rate) and epidemic parameters by using both the physics of epidemic spread (i.e. model) and the past epidemiological data. The closed-loop structure copes with the uncertainties and validates the estimation algorithm in real-time by predicting the future data and adjusting the epidemic model accordingly. The information received by the PINN-based observer will be utilized by the optimal controller to devise optimal policy recommendations under socio-economic constraints for epidemic mitigation. The closed-loop epidemic monitoring and control technique will be integrated to understand the geographic and demographic heterogeneities during epidemic outbreaks, which will significantly enhance the effectiveness of optimal policies.
Campo scientifico
Parole chiave
Programma(i)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Meccanismo di finanziamento
HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global FellowshipsCoordinatore
100 44 Stockholm
Svezia