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The design of efficient energy systems, through the development of new technologies and the improvement of current ones, requires the use of rigorous process synthesis methods for generating and analysing design alternatives. We introduce a digital twin of ...
A holistic approach, considering all the energy needs of a territory, should be adopted in the challenge of the energy transition. Synergies between the different end-use demand sectors must be developed, in order to optimize the efficient use of resources ...
The perception, physiology, behavior, and performance of building occupants are influenced by multi-domain exposures: the simultaneous presence of multiple environmental stimuli, i.e., visual, thermal, acoustic, and air quality. Despite being extensive, th ...
Data-driven and model-driven methodologies can be regarded as competitive fields since they tackle similar problems such as prediction. However, these two fields can learn from each other to improve themselves. Indeed, data-driven methodologies have been d ...
The practical implementation of photoelectrochemical devices for hydrogen generation is limited by their short lifetimes. Understanding the factors affecting the stability of the heterogeneous photoelectrodes is required to formulate degradation mitigation ...
Supply Chain Management (SCM), by way of one of the critical issues in the managerial aspect, plays a significant role in tackling humanitarian problems and difficulties. Due to some limitations (e.g., production capacity and storage capacity) and desires ...
Traffic congestion constitutes one of the most frequent, yet challenging, problems to address in the urban space. Caused by the concentration of population, whose mobility needs surpass the serving capacity of urban networks, congestion cannot be resolved ...
We generalize the hidden-fermion family of neural network quantum states to encompass both continuous and discrete degrees of freedom and solve the nuclear many-body Schrodinger equation in a systematically improvable fashion. We demonstrate that adding hi ...
In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M < N nodes. More precisely, we consider the setting in which the weights of the target network are i.i.d. sub-Gaussian, and we minimiz ...
In this thesis, we advocate that Computer-Aided Engineering could benefit from a Geometric Deep Learning revolution, similarly to the way that Deep Learning revolutionized Computer Vision. To do so, we consider a variety of Computer-Aided Engineering pro ...