Real-Time Model Calibration with Deep Reinforcement Learning

no code implementations • 7 Jun 2020 • Yuan Tian, Manuel Arias Chao, Chetan Kulkarni, Kai Goebel, Olga Fink

The dynamic, real-time, and accurate inference of model parameters from empirical data is of great importance in many scientific and engineering disciplines that use computational models (such as a digital twin) for the analysis and prediction of complex physical processes.

reinforcement-learning Reinforcement Learning (RL)

A Prescriptive Dirichlet Power Allocation Policy with Deep Reinforcement Learning

no code implementations • 20 Jan 2022 • Yuan Tian, Minghao Han, Chetan Kulkarni, Olga Fink

Moreover, we demonstrate the applicability of the proposed algorithm on a prescriptive operation case, where we propose the Dirichlet power allocation policy and evaluate the performance on a case study of a set of multiple lithium-ion (Li-I) battery systems.

reinforcement-learning Reinforcement Learning (RL)

Dynaformer: A Deep Learning Model for Ageing-aware Battery Discharge Prediction

1 code implementation • 1 Jun 2022 • Luca Biggio, Tommaso Bendinelli, Chetan Kulkarni, Olga Fink

Electrochemical batteries are ubiquitous devices in our society.

Decision Making Management

Prescribing Optimal Health-Aware Operation for Urban Air Mobility with Deep Reinforcement Learning

no code implementations • 12 Apr 2024 • Mina Montazeri, Chetan Kulkarni, Olga Fink

This paper addresses the joint problem of mission planning and health-aware real-time control of opera-tional parameters to prescriptively control the duration of one discharge cycle of the battery pack.