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Found 4 papers, 0 papers with code
Diffusion bridges vector quantized Variational AutoEncoders
In this work, we propose a new model to train the prior and the encoder/decoder networks simultaneously.
End-to-end deep meta modelling to calibrate and optimize energy consumption and comfort
Parameters are estimated by comparing the predictions of the metamodel with real data obtained from sensors using the CMA-ES algorithm, a derivative free optimization procedure.
Model-Based Reinforcement Learning for Approximate Optimal Control with Temporal Logic Specifications
In this paper we study the problem of synthesizing optimal control policies for uncertain continuous-time nonlinear systems from syntactically co-safe linear temporal logic (scLTL) formulas.
End-to-end deep metamodeling to calibrate and optimize energy loads
Finally, the optimal settings to minimize the energy loads while maintaining a target thermal comfort and air quality are obtained using a multi-objective optimization procedure.
