Tensor network theory and quantum simulation are respectively the key classical computing methods in understanding many-body physics. Here, we introduce framework of hybrid tensor networks with building blocks consisting measurable states classically contractable tensors, inheriting both their distinct features efficient representation wave functions. With example tree networks, demonstrate usi...

Markov logic networks (MLNs) combine first-order logic and Markov networks, allowing us to handle the complexity and uncertainty of real-world problems in a single consistent framework. However, in MLNs all variables and features are discrete, while most real-world applications also contain continuous ones. In this paper we introduce hybrid MLNs, in which continuous properties (e.g., the distan...

Future wireless Am networks are expected to support a variety of services with different bit-rates and Quality-of-Service (QoS) requirements. A major challenge for these networks is the de= sign of the multiple access scheme. In our previous works, we proposed hybrid CDMA/TDMA technique as an appropriate multiple access control (MAC) scheme for wireless Am networks. We also investigated the pro...

this paper presents a hybrid approach to developing a short-term traffic flow prediction model. in thisapproach a primary model is synthesized based on neural networks and then the model structure is optimized throughgenetic algorithm. the proposed approach is applied to a rural highway, ghazvin-rasht road in iran. the obtainedresults are acceptable and indicate that the proposed approach can i...

Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The prop...

Increasing the short circuit current due to the penetration of distributed generations (DGs) in various voltage levels and meshed topology is a basic problem in power systems. Using fault current limiter (FCL) is an efficient approach to mitigate the exceeded short circuit levels. In this paper, a new approach is presented for multiple FCLs locating to decrease short circuit levels in meshed ne...

Optical neural networks are emerging as a promising type of machine learning hardware capable energy-efficient, parallel computation. Today’s optical mainly developed to perform inference after in silico training on digital simulators. However, various physical imperfections that cannot be accurately modeled may lead the notorious “reality gap” between simulator and system. To address this chal...